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<title>No One Mourns the Wicked: The Limits of Partisan Hostility Persisting through Tragedy</title>

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) 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<h1 class="title toc-ignore">No One Mourns the Wicked: The Limits of
Partisan Hostility Persisting through Tragedy</h1>
<h4 class="author">Wayde Z.C. Marsh</h4>
<h4 class="date">10/31/24</h4>

</div>

<div id="TOC">
<ul>
<li><a href="#setting-up-the-data" id="toc-setting-up-the-data">Setting
up the Data</a>
<ul>
<li><a href="#loading-packages-and-data" id="toc-loading-packages-and-data">Loading packages and data</a></li>
</ul></li>
<li><a href="#raw-data" id="toc-raw-data">Raw Data</a></li>
<li><a href="#analysis" id="toc-analysis">Analysis</a>
<ul>
<li><a href="#modeling-effects" id="toc-modeling-effects">Modeling
Effects</a></li>
<li><a href="#dims-tukey-multiple-pairwise-comparisons" id="toc-dims-tukey-multiple-pairwise-comparisons">DIMs (Tukey Multiple
pairwise-comparisons)</a></li>
</ul></li>
<li><a href="#summary-statistics-and-diagnostics" id="toc-summary-statistics-and-diagnostics">Summary Statistics and
Diagnostics</a>
<ul>
<li><a href="#survey-stats" id="toc-survey-stats">Survey Stats</a></li>
<li><a href="#means-of-dvs" id="toc-means-of-dvs">Means of DVs</a></li>
<li><a href="#balance-analysis" id="toc-balance-analysis">Balance
Analysis</a></li>
</ul></li>
<li><a href="#additionalsecondary-figures-and-analysis" id="toc-additionalsecondary-figures-and-analysis">Additional/Secondary
Figures and Analysis</a>
<ul>
<li><a href="#figures" id="toc-figures">Figures</a></li>
<li><a href="#alternative-modeling-approaches" id="toc-alternative-modeling-approaches">Alternative Modeling
Approaches</a></li>
<li><a href="#code-graveyard" id="toc-code-graveyard">Code
Graveyard</a></li>
</ul></li>
</ul>
</div>

<div id="setting-up-the-data" class="section level1 tabset">
<h1 class="tabset">Setting up the Data</h1>
<div id="loading-packages-and-data" class="section level2">
<h2>Loading packages and data</h2>
<pre class="r"><code>##### Packages #####

library(descr)
library(ggplot2)
library(dplyr)
library(foreign)
library(plyr)
library(ggpubr)
library(ggeffects)
library(tidyverse)
library(haven)
library(modelsummary)
library(Rmisc)
library(xtable)
options(&quot;modelsummary_format_numeric_latex&quot; = &quot;mathmode&quot;)


# setwd(dirname(rstudioapi::getSourceEditorContext()$path)) 
# knitr::opts_knit$set(root.dir = &#39;~/&#39;)</code></pre>
</div>
</div>
<div id="raw-data" class="section level1">
<h1>Raw Data</h1>
<pre class="r"><code># load Lucid data

df &lt;- read.csv(&#39;CES21_pilot1.csv&#39;)

# clean Lucid Data

df$Ind.Leaners &lt;- as.numeric(df$Ind.Leaners)
df$Dem.Party.Strength &lt;- as.numeric(df$Dem.Party.Strength)
df$Rep.Party.Strength &lt;- as.numeric(df$Rep.Party.Strength)

df$pid &lt;- ifelse((df$Party.ID.1 == 1 &amp; df$Dem.Party.Strength == 1), 1,
          ifelse((df$Party.ID.1 == 1 &amp; df$Dem.Party.Strength == 2), 2,
          ifelse((df$Party.ID.1 == 3 &amp; df$Ind.Leaners == 2) | 
                 (df$Party.ID.1 == 4 &amp; df$Ind.Leaners == 2) |
                 (df$Party.ID.1 == 5 &amp; df$Ind.Leaners == 2), 3,
          ifelse((df$Party.ID.1 == 3 &amp; df$Ind.Leaners == 3) | 
                 (df$Party.ID.1 == 4 &amp; df$Ind.Leaners == 3) |
                 (df$Party.ID.1 == 5 &amp; df$Ind.Leaners == 3) |
                 (df$Party.ID.1 == 3 &amp; df$Ind.Leaners == 4) | 
                 (df$Party.ID.1 == 4 &amp; df$Ind.Leaners == 4) |
                 (df$Party.ID.1 == 5 &amp; df$Ind.Leaners == 4), 4,
          ifelse((df$Party.ID.1 == 3 &amp; df$Ind.Leaners == 1) | 
                 (df$Party.ID.1 == 4 &amp; df$Ind.Leaners == 1) |
                 (df$Party.ID.1 == 5 &amp; df$Ind.Leaners == 1), 5,
          ifelse((df$Party.ID.1 == 2 &amp; df$Rep.Party.Strength == 2), 6,
          ifelse((df$Party.ID.1 == 2 &amp; df$Rep.Party.Strength == 1), 7, NA)))))))

df$gop &lt;- ifelse(df$pid &gt; 4, 1,
          ifelse(df$pid &lt; 4, 0, NA))

## Creating treatment variable

df$tr2_ct &lt;- ifelse(is.na(df$study2_control_time_Page.Submit), 0, 1)
df$tr2_bl &lt;- ifelse(is.na(df$study2_black_time_Page.Submit), 0, 1)
df$tr2_lx &lt;- ifelse(is.na(df$study2_latino_time_Page.Submit), 0, 1)
df$tr2_wh &lt;- ifelse(is.na(df$study2_white_time_Page.Submit), 0, 1)

df$tr2 &lt;- ifelse(df$tr2_ct == 1, 1,
          ifelse(df$tr2_bl == 1, 2,
          ifelse(df$tr2_lx == 1, 3,
          ifelse(df$tr2_wh == 1, 4, NA))))

## Creating treatment variable

df$tr1_ct &lt;- ifelse(is.na(df$study1_control_time_Page.Submit), 0, 1)
df$tr1_anti &lt;- ifelse(is.na(df$study1_antivax_time_Page.Submit), 0, 1)
df$tr1_dem &lt;- ifelse(is.na(df$study1_dem_time_Page.Submit), 0, 1)
df$tr1_rep &lt;- ifelse(is.na(df$study1_rep_time_Page.Submit), 0, 1)
df$tr1_antidem &lt;- ifelse(is.na(df$study1_antidem_time_Page.Submit), 0, 1)
df$tr1_antirep &lt;- ifelse(is.na(df$study1_antirep_time_Page.Submit), 0, 1)

df$tr1 &lt;- ifelse(df$tr1_ct == 1, 1,
          ifelse(df$tr1_anti == 1, 2,
          ifelse(df$tr1_dem == 1, 3,
          ifelse(df$tr1_rep == 1, 4,
          ifelse(df$tr1_antidem == 1, 5,
          ifelse(df$tr1_antirep == 1, 6, NA))))))

df$CaseID &lt;- 1:nrow(df)

## Combine DVs

df$study2_dv1 &lt;- rowSums(cbind(df$study2_dv_control_1,
                                df$study2_dv_black_1,
                                df$study2_dv_latino_1,
                                df$study2_dv_white_1), na.rm = T)
df$study2_dv2 &lt;- rowSums(cbind(df$study2_dv_control_2,
                                df$study2_dv_black_2,
                                df$study2_dv_latino_2,
                                df$study2_dv_white_2), na.rm = T)
df$study2_dv3 &lt;- rowSums(cbind(df$study2_dv_control_3,
                                df$study2_dv_black_3,
                                df$study2_dv_latino_3,
                                df$study2_dv_white_3), na.rm = T)
df$study2_dv4 &lt;- rowSums(cbind(df$study2_dv_control_4,
                                df$study2_dv_black_4,
                                df$study2_dv_latino_4,
                                df$study2_dv_white_4), na.rm = T)
df$study2_dv_sym &lt;- rowSums(cbind(df$study2_dv2_control_1,
                                df$study2_dv2_black_1,
                                df$study2_dv2_latino_1,
                                df$study2_dv2_white_1), na.rm = T)

# load CES data

df2 &lt;- read_sav(&#39;CCES21_NDB_OUTPUT.sav&#39;)

# clean CES data

df2$trgrp &lt;- df2$UND452

df2$study1_dv_1 &lt;- df2$UND453
df2$study1_dv_2 &lt;- df2$UND454
df2$study1_dv_3 &lt;- df2$UND455
df2$study1_dv2_1 &lt;- df2$UND456

df2$mandate &lt;- df2$UND448
df2$covidconcern &lt;- df2$UND449
df2$vaxstatus &lt;- df2$CC21_306

df2$pid &lt;- df2$pid7
df2$pid &lt;- ifelse(df2$pid &gt; 7, NA, df2$pid)
df2$gop &lt;- ifelse(df2$pid &gt; 4, 1,
                 ifelse(df2$pid &lt; 4, 0, NA))

df2$coviddeath &lt;- ifelse((df2$CC21_305b_1 == 1 | df2$CC21_305b_2 == 1) |
                           df2$CC21_305b_3 == 1, 1, 2)
df2$coviddeath &lt;- ifelse(is.na(df2$coviddeath), 2, df2$coviddeath)

df$black &lt;- ifelse(df$ethnicity == 2, 1, 0)
df2$black &lt;- ifelse(df2$race == 2, 1, 0)
df$hispanic &lt;- ifelse(df$hispanic == 2, 1, 0)
df2$hispanic &lt;- ifelse(df2$hispanic == 1, 1, 0)
df$female &lt;- ifelse(df$gender == 2, 1, 0)
df2$female &lt;- ifelse(df2$gender4 == 2, 1, 0)

df2$age &lt;- 2021 - df2$birthyr

df$education &lt;- ifelse(df$education &lt; 0, NA,
                ifelse(df$education == 3, 2,
                ifelse((df$education == 4 | df$education == 5), 3,
                ifelse(df$education == 6, 4,
                ifelse(df$education == 7, 5, 
                ifelse(df$education == 8, 6, df$education))))))

df2$covidconcern &lt;- df2$UND449
df2$vaxstatus &lt;- df2$CC21_306

df &lt;- df[, c(&#39;CaseID&#39;, &#39;age&#39;, &#39;female&#39;, &#39;black&#39;, &#39;hispanic&#39;, &#39;education&#39;,
             &#39;tr1&#39;, &#39;study1_dv_1&#39;, &#39;study1_dv_2&#39;, &#39;study1_dv2_1&#39;, &#39;study1_dv_3&#39;,
             &#39;pid&#39;, &#39;gop&#39;, &#39;coviddeath&#39;, &#39;vaxstatus&#39;, &#39;covidconcern_1&#39;, &#39;covidvaccine_1&#39;)]

df2 &lt;- df2[, c(&#39;caseid&#39;, &#39;age&#39;, &#39;female&#39;, &#39;black&#39;, &#39;hispanic&#39;, &#39;educ&#39;,
             &#39;trgrp&#39;, &#39;study1_dv_1&#39;, &#39;study1_dv_2&#39;, &#39;study1_dv2_1&#39;, &#39;study1_dv_3&#39;,
             &#39;pid&#39;, &#39;gop&#39;, &#39;teamweight&#39;, &#39;coviddeath&#39;, &#39;vaxstatus&#39;, &#39;covidconcern&#39;, &#39;mandate&#39;)]

prop &lt;- function(x, na.rm = FALSE) (x /100) # create function to turn 0-100 scales to 0-1

df &lt;- df %&gt;%
  mutate(across(c(&#39;study1_dv_1&#39;, &#39;study1_dv_2&#39;, &#39;study1_dv2_1&#39;, &#39;study1_dv_3&#39;,
                  &#39;covidconcern_1&#39;, &#39;covidvaccine_1&#39;), prop)) # convert 0-100 to 0-1 in study 1
df2 &lt;- df2 %&gt;%
  mutate(across(c(&#39;study1_dv_1&#39;, &#39;study1_dv_2&#39;, &#39;study1_dv2_1&#39;, &#39;study1_dv_3&#39;,
                  &#39;covidconcern&#39;, &#39;mandate&#39;), prop)) # convert 0-100 to 0-1 in study 2

# Create treatment variables

df$tr_dem &lt;- ifelse(df$tr1 == 3 | df$tr1 == 5, 1, 0)
df$tr_rep &lt;- ifelse(df$tr1 == 4 | df$tr1 == 6, 1, 0)
df$tr_pid &lt;- ifelse(df$tr1 == 3 | df$tr1 == 5, 1, 
             ifelse(df$tr1 == 4 | df$tr1 == 6, 2, 0))
df$tr_vax &lt;- ifelse(df$tr1 == 2 | df$tr1 &gt; 4, 1, 0)

df2$tr_dem &lt;- ifelse(df2$trgrp == 3 | df2$trgrp == 5, 1, 0)
df2$tr_rep &lt;- ifelse(df2$trgrp == 4 | df2$trgrp == 6, 1, 0)
df2$tr_pid &lt;- ifelse(df2$trgrp == 3 | df2$trgrp == 5, 1, 
             ifelse(df2$trgrp == 4 | df2$trgrp == 6, 2, 0))
df2$tr_vax &lt;- ifelse(df2$trgrp == 2 | df2$trgrp &gt; 4, 1, 0)

df$coviddeath &lt;- ifelse(df$coviddeath == 2, 0, df$coviddeath) # simplifies covid deat and vaccination status variables
df2$coviddeath &lt;- ifelse(df2$coviddeath == 2, 0, df2$coviddeath)
df2$vaxstatus &lt;- ifelse(df2$vaxstatus == 2, 0, df2$vaxstatus)
df$vaxstatus2 &lt;- ifelse(df$vaxstatus == 3, 0,
                 ifelse(df$vaxstatus &gt; 4, NA, 1))</code></pre>
</div>
<div id="analysis" class="section level1">
<h1>Analysis</h1>
<div id="modeling-effects" class="section level2">
<h2>Modeling Effects</h2>
<pre class="r"><code># scale &lt;- function(x){x/100}
# vars &lt;- c(&#39;study1_dv_1&#39;, &#39;study1_dv_2&#39;, &#39;study1_dv2_1&#39;, &#39;study1_dv_3&#39;)
# df &lt;- df %&gt;% 
#   mutate_at(vars, scale)
# df2 &lt;- df2 %&gt;% 
#   mutate_at(vars, scale)

## Main Analysis (3x2 design)
# models for table 2
# study I
mod1_1 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = df) 
summary(mod1_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.72489 -0.29606  0.02957  0.28061  0.71830 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.467151   0.022060  21.176  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.014358   0.029683  -0.484    0.629    
## factor(tr_pid)2                  0.037867   0.029738   1.273    0.203    
## factor(tr_vax)1                  0.274372   0.029276   9.372  &lt; 2e-16 ***
## pid                             -0.024441   0.003909  -6.253  5.4e-10 ***
## factor(tr_pid)1:factor(tr_vax)1  0.022168   0.045345   0.489    0.625    
## factor(tr_pid)2:factor(tr_vax)1 -0.030764   0.045057  -0.683    0.495    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3346 on 1334 degrees of freedom
##   (103 observations deleted due to missingness)
## Multiple R-squared:  0.1609, Adjusted R-squared:  0.1571 
## F-statistic: 42.64 on 6 and 1334 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod1_dem_1 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df, gop == 0)) 
summary(mod1_dem_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax), data = subset(df, 
##     gop == 0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.73337 -0.25920  0.04663  0.27604  0.60031 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.43929    0.02320  18.937  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.03960    0.04031  -0.982   0.3263    
## factor(tr_pid)2                  0.08264    0.04103   2.014   0.0444 *  
## factor(tr_vax)1                  0.28466    0.04134   6.886 1.35e-11 ***
## factor(tr_pid)1:factor(tr_vax)1  0.03457    0.06375   0.542   0.5878    
## factor(tr_pid)2:factor(tr_vax)1 -0.07323    0.06278  -1.166   0.2439    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3264 on 656 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.1573, Adjusted R-squared:  0.1509 
## F-statistic: 24.49 on 5 and 656 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod1_gop_1 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df, gop == 1)) 
summary(mod1_gop_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax), data = subset(df, 
##     gop == 1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.61260 -0.32042 -0.04161  0.29727  0.67958 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.3333835  0.0297173  11.219  &lt; 2e-16 ***
## factor(tr_pid)1                  0.0006464  0.0513436   0.013    0.990    
## factor(tr_pid)2                 -0.0129609  0.0503727  -0.257    0.797    
## factor(tr_vax)1                  0.2397334  0.0490765   4.885 1.41e-06 ***
## factor(tr_pid)1:factor(tr_vax)1  0.0388341  0.0754118   0.515    0.607    
## factor(tr_pid)2:factor(tr_vax)1  0.0504594  0.0766145   0.659    0.510    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3427 on 484 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1338, Adjusted R-squared:  0.1249 
## F-statistic: 14.95 on 5 and 484 DF,  p-value: 1.162e-13</code></pre>
<pre class="r"><code>mod1_ind_1 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df, pid == 4)) 
summary(mod1_ind_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax), data = subset(df, 
##     pid == 4))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63276 -0.27702 -0.02702  0.26000  0.72298 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.277018   0.043965   6.301 2.15e-09 ***
## factor(tr_pid)1                  0.036732   0.080768   0.455    0.650    
## factor(tr_pid)2                  0.003816   0.080768   0.047    0.962    
## factor(tr_vax)1                  0.355741   0.075710   4.699 5.13e-06 ***
## factor(tr_pid)1:factor(tr_vax)1 -0.119491   0.122120  -0.978    0.329    
## factor(tr_pid)2:factor(tr_vax)1 -0.103026   0.117799  -0.875    0.383    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3319 on 183 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1659, Adjusted R-squared:  0.1431 
## F-statistic: 7.281 on 5 and 183 DF,  p-value: 3.035e-06</code></pre>
<pre class="r"><code>mod1_ind2_1 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df, pid &gt; 2 &amp; pid &lt; 6)) 
summary(mod1_ind2_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax), data = subset(df, 
##     pid &gt; 2 &amp; pid &lt; 6))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63951 -0.29376 -0.03077  0.26642  0.70624 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                     0.293761   0.031091   9.448  &lt; 2e-16 ***
## factor(tr_pid)1                 0.001795   0.058991   0.030    0.976    
## factor(tr_pid)2                 0.069636   0.055683   1.251    0.212    
## factor(tr_vax)1                 0.267009   0.056051   4.764 2.74e-06 ***
## factor(tr_pid)1:factor(tr_vax)1 0.074670   0.089785   0.832    0.406    
## factor(tr_pid)2:factor(tr_vax)1 0.009103   0.084438   0.108    0.914    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3363 on 369 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1736, Adjusted R-squared:  0.1624 
## F-statistic:  15.5 on 5 and 369 DF,  p-value: 7.541e-14</code></pre>
<pre class="r"><code># creates table 2
modelsummary(models = list(&#39;Full Sample&#39;             = mod1_1,
                           &#39;Democrats&#39;               = mod1_dem_1,
                           &#39;Republicans&#39;             = mod1_gop_1,
                           &#39;Independents&#39;            = mod1_ind_1,
                           &#39;Independents w/ Leaners&#39; = mod1_ind2_1),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code># models for table 4
mod2_1 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = df) 
summary(mod2_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.72915 -0.21914  0.02881  0.26925  0.50683 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.719591   0.019732  36.468  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.006848   0.026622  -0.257    0.797    
## factor(tr_pid)2                 -0.104501   0.026623  -3.925 9.11e-05 ***
## factor(tr_vax)1                 -0.188163   0.026165  -7.191 1.07e-12 ***
## pid                              0.001594   0.003498   0.456    0.649    
## factor(tr_pid)1:factor(tr_vax)1  0.006569   0.040588   0.162    0.871    
## factor(tr_pid)2:factor(tr_vax)1  0.064646   0.040359   1.602    0.109    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2998 on 1336 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.08864,    Adjusted R-squared:  0.08454 
## F-statistic: 21.66 on 6 and 1336 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod2_dem_1 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df, gop == 0)) 
summary(mod2_dem_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax), 
##     data = subset(df, gop == 0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.78536 -0.21878  0.02857  0.22735  0.54439 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.73613    0.02118  34.755  &lt; 2e-16 ***
## factor(tr_pid)1                  0.04923    0.03700   1.331   0.1838    
## factor(tr_pid)2                 -0.18226    0.03753  -4.856 1.50e-06 ***
## factor(tr_vax)1                 -0.20559    0.03767  -5.458 6.84e-08 ***
## factor(tr_pid)1:factor(tr_vax)1 -0.02835    0.05833  -0.486   0.6272    
## factor(tr_pid)2:factor(tr_vax)1  0.10733    0.05736   1.871   0.0618 .  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2988 on 657 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1395, Adjusted R-squared:  0.133 
## F-statistic:  21.3 on 5 and 657 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod2_gop_1 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df, gop == 1)) 
summary(mod2_gop_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax), 
##     data = subset(df, gop == 1))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7606 -0.2215  0.0194  0.2394  0.4574 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.76060    0.02474  30.743  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.12284    0.04285  -2.867  0.00433 ** 
## factor(tr_pid)2                 -0.05820    0.04204  -1.384  0.16684    
## factor(tr_vax)1                 -0.21358    0.04095  -5.215 2.72e-07 ***
## factor(tr_pid)1:factor(tr_vax)1  0.11839    0.06287   1.883  0.06030 .  
## factor(tr_pid)2:factor(tr_vax)1  0.09103    0.06414   1.419  0.15646    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2864 on 485 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.08838,    Adjusted R-squared:  0.07899 
## F-statistic: 9.404 on 5 and 485 DF,  p-value: 1.439e-08</code></pre>
<pre class="r"><code>mod2_ind_1 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df, pid == 4)) 
summary(mod2_ind_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax), 
##     data = subset(df, pid == 4))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.64333 -0.21333  0.01724  0.31129  0.54129 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.60474    0.04163  14.528   &lt;2e-16 ***
## factor(tr_pid)1                  0.06860    0.07647   0.897    0.371    
## factor(tr_pid)2                  0.03526    0.07647   0.461    0.645    
## factor(tr_vax)1                 -0.07198    0.07168  -1.004    0.317    
## factor(tr_pid)1:factor(tr_vax)1 -0.12927    0.11562  -1.118    0.265    
## factor(tr_pid)2:factor(tr_vax)1 -0.10931    0.11153  -0.980    0.328    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3143 on 183 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.05708,    Adjusted R-squared:  0.03131 
## F-statistic: 2.215 on 5 and 183 DF,  p-value: 0.0546</code></pre>
<pre class="r"><code>mod2_ind2_1 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df, pid &gt; 2 &amp; pid &lt; 6)) 
summary(mod2_ind2_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax), 
##     data = subset(df, pid &gt; 2 &amp; pid &lt; 6))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.70422 -0.21803  0.03197  0.29578  0.53164 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.66803    0.02900  23.037   &lt;2e-16 ***
## factor(tr_pid)1                  0.03619    0.05502   0.658   0.5111    
## factor(tr_pid)2                 -0.08105    0.05193  -1.561   0.1195    
## factor(tr_vax)1                 -0.09073    0.05228  -1.736   0.0835 .  
## factor(tr_pid)1:factor(tr_vax)1 -0.14499    0.08374  -1.731   0.0842 .  
## factor(tr_pid)2:factor(tr_vax)1 -0.02789    0.07875  -0.354   0.7234    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3137 on 369 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.07408,    Adjusted R-squared:  0.06153 
## F-statistic: 5.904 on 5 and 369 DF,  p-value: 2.887e-05</code></pre>
<pre class="r"><code># creates table 4
modelsummary(models = list(&#39;Full Sample&#39;             = mod2_1,
                           &#39;Democrats&#39;               = mod2_dem_1,
                           &#39;Republicans&#39;             = mod2_gop_1,
                           &#39;Independents&#39;            = mod2_ind_1,
                           &#39;Independents w/ Leaners&#39; = mod2_ind2_1),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code># study II
# models for table 3
mod1_2 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = df2, weights = teamweight) 
summary(mod1_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = df2, weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.91463 -0.17750  0.04155  0.21144  1.18202 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.564023   0.029188  19.324  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.040398   0.034215  -1.181   0.2380    
## factor(tr_pid)2                  0.056963   0.032891   1.732   0.0836 .  
## factor(tr_vax)1                  0.205977   0.034249   6.014 2.59e-09 ***
## pid                             -0.046341   0.004751  -9.754  &lt; 2e-16 ***
## factor(tr_pid)1:factor(tr_vax)1  0.056685   0.049098   1.155   0.2486    
## factor(tr_pid)2:factor(tr_vax)1  0.064247   0.048827   1.316   0.1886    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3048 on 942 degrees of freedom
##   (51 observations deleted due to missingness)
## Multiple R-squared:  0.2245, Adjusted R-squared:  0.2196 
## F-statistic: 45.45 on 6 and 942 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod1_dem_2 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df2, gop == 0), weights = teamweight) 
summary(mod1_dem_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax), data = subset(df2, 
##     gop == 0), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.84023 -0.12635  0.06495  0.17266  0.71935 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.48259    0.03071  15.713  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.04296    0.04606  -0.933   0.3515    
## factor(tr_pid)2                  0.13712    0.04670   2.936   0.0035 ** 
## factor(tr_vax)1                  0.21295    0.04551   4.679 3.84e-06 ***
## factor(tr_pid)1:factor(tr_vax)1  0.07365    0.06470   1.138   0.2556    
## factor(tr_pid)2:factor(tr_vax)1  0.05421    0.06612   0.820   0.4127    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2714 on 442 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2254, Adjusted R-squared:  0.2167 
## F-statistic: 25.73 on 5 and 442 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod1_gop_2 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df2, gop == 1), weights = teamweight) 
summary(mod1_gop_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax), data = subset(df2, 
##     gop == 1), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.76838 -0.25111 -0.00589  0.25055  0.99664 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.32306    0.04348   7.431 1.06e-12 ***
## factor(tr_pid)1                 -0.02922    0.06366  -0.459  0.64651    
## factor(tr_pid)2                 -0.02417    0.05873  -0.412  0.68097    
## factor(tr_vax)1                  0.18327    0.06273   2.922  0.00374 ** 
## factor(tr_pid)1:factor(tr_vax)1  0.01209    0.09175   0.132  0.89528    
## factor(tr_pid)2:factor(tr_vax)1  0.01764    0.08785   0.201  0.84096    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3293 on 310 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.08342,    Adjusted R-squared:  0.06864 
## F-statistic: 5.643 on 5 and 310 DF,  p-value: 5.376e-05</code></pre>
<pre class="r"><code>mod1_ind_2 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df2, pid == 4), weights = teamweight) 
summary(mod1_ind_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax), data = subset(df2, 
##     pid == 4), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.82348 -0.18389  0.02164  0.22091  1.32178 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.30520    0.05131   5.948  1.4e-08 ***
## factor(tr_pid)1                 -0.03467    0.07382  -0.470  0.63922    
## factor(tr_pid)2                  0.07827    0.06987   1.120  0.26413    
## factor(tr_vax)1                  0.25206    0.07789   3.236  0.00144 ** 
## factor(tr_pid)1:factor(tr_vax)1  0.01396    0.11118   0.126  0.90024    
## factor(tr_pid)2:factor(tr_vax)1  0.05491    0.11389   0.482  0.63030    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3117 on 179 degrees of freedom
## Multiple R-squared:  0.1769, Adjusted R-squared:  0.1539 
## F-statistic: 7.691 on 5 and 179 DF,  p-value: 1.422e-06</code></pre>
<pre class="r"><code>mod1_ind2_2 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df2, pid &gt; 2 &amp; pid &lt; 6), weights = teamweight) 
summary(mod1_ind2_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax), data = subset(df2, 
##     pid &gt; 2 &amp; pid &lt; 6), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.02240 -0.21444  0.02878  0.21920  1.27186 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.331433   0.041149   8.054  1.3e-14 ***
## factor(tr_pid)1                 -0.002818   0.060015  -0.047 0.962577    
## factor(tr_pid)2                  0.070056   0.056181   1.247 0.213255    
## factor(tr_vax)1                  0.238085   0.060742   3.920 0.000107 ***
## factor(tr_pid)1:factor(tr_vax)1 -0.035905   0.086615  -0.415 0.678740    
## factor(tr_pid)2:factor(tr_vax)1  0.084953   0.084490   1.005 0.315367    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3294 on 346 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1572, Adjusted R-squared:  0.145 
## F-statistic: 12.91 on 5 and 346 DF,  p-value: 1.599e-11</code></pre>
<pre class="r"><code># creates table 3
modelsummary(models = list(&#39;Full Sample&#39;             = mod1_2,
                           &#39;Democrats&#39;               = mod1_dem_2,
                           &#39;Republicans&#39;             = mod1_gop_2,
                           &#39;Independents&#39;            = mod1_ind_2,
                           &#39;Independents w/ Leaners&#39; = mod1_ind2_2),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code># models for table 5
mod2_2 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = df2, weights = teamweight) 
summary(mod2_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = df2, weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.69935 -0.18919  0.01012  0.19547  1.45380 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.562451   0.028408  19.799  &lt; 2e-16 ***
## factor(tr_pid)1                  0.045824   0.033305   1.376   0.1692    
## factor(tr_pid)2                 -0.059707   0.032007  -1.865   0.0624 .  
## factor(tr_vax)1                 -0.136476   0.033297  -4.099 4.51e-05 ***
## pid                              0.024542   0.004612   5.321 1.29e-07 ***
## factor(tr_pid)1:factor(tr_vax)1 -0.057483   0.047767  -1.203   0.2291    
## factor(tr_pid)2:factor(tr_vax)1 -0.022710   0.047494  -0.478   0.6326    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2971 on 945 degrees of freedom
##   (48 observations deleted due to missingness)
## Multiple R-squared:  0.1079, Adjusted R-squared:  0.1022 
## F-statistic: 19.05 on 6 and 945 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod2_dem_2 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df2, gop == 0), weights = teamweight) 
summary(mod2_dem_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax), 
##     data = subset(df2, gop == 0), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.86496 -0.21579 -0.00948  0.15449  1.49796 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.718508   0.032035  22.429  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.002313   0.048046  -0.048    0.962    
## factor(tr_pid)2                 -0.234117   0.048711  -4.806 2.11e-06 ***
## factor(tr_vax)1                 -0.284682   0.047338  -6.014 3.80e-09 ***
## factor(tr_pid)1:factor(tr_vax)1 -0.010223   0.067393  -0.152    0.880    
## factor(tr_pid)2:factor(tr_vax)1  0.103489   0.068990   1.500    0.134    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2831 on 442 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2204, Adjusted R-squared:  0.2115 
## F-statistic: 24.99 on 5 and 442 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod2_gop_2 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df2, gop == 1), weights = teamweight) 
summary(mod2_gop_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax), 
##     data = subset(df2, gop == 1), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.50407 -0.14856  0.03336  0.22877  0.58626 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.628149   0.038456  16.334   &lt;2e-16 ***
## factor(tr_pid)1                  0.118055   0.056578   2.087   0.0377 *  
## factor(tr_pid)2                  0.059301   0.052162   1.137   0.2565    
## factor(tr_vax)1                 -0.002086   0.055744  -0.037   0.9702    
## factor(tr_pid)1:factor(tr_vax)1 -0.138916   0.081544  -1.704   0.0895 .  
## factor(tr_pid)2:factor(tr_vax)1 -0.105844   0.077881  -1.359   0.1751    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2939 on 313 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.03441,    Adjusted R-squared:  0.01898 
## F-statistic: 2.231 on 5 and 313 DF,  p-value: 0.05115</code></pre>
<pre class="r"><code>mod2_ind_2 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df2, pid == 4), weights = teamweight) 
summary(mod2_ind_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax), 
##     data = subset(df2, pid == 4), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.04587 -0.16511  0.00006  0.21571  0.70734 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.55977    0.04982  11.236   &lt;2e-16 ***
## factor(tr_pid)1                  0.04399    0.07167   0.614    0.540    
## factor(tr_pid)2                  0.07224    0.06784   1.065    0.288    
## factor(tr_vax)1                 -0.05998    0.07562  -0.793    0.429    
## factor(tr_pid)1:factor(tr_vax)1  0.01612    0.10794   0.149    0.881    
## factor(tr_pid)2:factor(tr_vax)1 -0.05647    0.11057  -0.511    0.610    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3026 on 179 degrees of freedom
## Multiple R-squared:  0.0246, Adjusted R-squared:  -0.002646 
## F-statistic: 0.9029 on 5 and 179 DF,  p-value: 0.4805</code></pre>
<pre class="r"><code>mod2_ind2_2 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax), data = subset(df2, pid &gt; 2 &amp; pid &lt; 6), weights = teamweight) 
summary(mod2_ind2_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax), 
##     data = subset(df2, pid &gt; 2 &amp; pid &lt; 6), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.12218 -0.15358  0.01944  0.18992  0.74775 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.599884   0.038286  15.668   &lt;2e-16 ***
## factor(tr_pid)1                  0.005985   0.055839   0.107    0.915    
## factor(tr_pid)2                  0.007159   0.052272   0.137    0.891    
## factor(tr_vax)1                 -0.089932   0.056325  -1.597    0.111    
## factor(tr_pid)1:factor(tr_vax)1  0.060653   0.080455   0.754    0.451    
## factor(tr_pid)2:factor(tr_vax)1 -0.067101   0.078474  -0.855    0.393    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3064 on 347 degrees of freedom
## Multiple R-squared:  0.03546,    Adjusted R-squared:  0.02156 
## F-statistic: 2.551 on 5 and 347 DF,  p-value: 0.02762</code></pre>
<pre class="r"><code># creates table 5
modelsummary(models = list(&#39;Full Sample&#39;             = mod2_2,
                           &#39;Democrats&#39;               = mod2_dem_2,
                           &#39;Republicans&#39;             = mod2_gop_2,
                           &#39;Independents&#39;            = mod2_ind_2,
                           &#39;Independents w/ Leaners&#39; = mod2_ind2_2),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code># Vax Status Models
# models for table 6
# study I
mod_vax &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax) + pid + vaxstatus2 + coviddeath + covidconcern_1 + covidvaccine_1, data = df)
summary(mod_vax)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid + vaxstatus2 + coviddeath + covidconcern_1 + covidvaccine_1, 
##     data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.86980 -0.26274  0.02228  0.23869  0.92652 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.118559   0.035536   3.336 0.000873 ***
## factor(tr_pid)1                 -0.006334   0.028437  -0.223 0.823789    
## factor(tr_pid)2                  0.053219   0.028605   1.860 0.063050 .  
## factor(tr_vax)1                  0.286194   0.028308  10.110  &lt; 2e-16 ***
## pid                             -0.006440   0.004038  -1.595 0.110972    
## vaxstatus2                       0.103236   0.022819   4.524 6.63e-06 ***
## coviddeath                       0.071949   0.020241   3.555 0.000392 ***
## covidconcern_1                   0.099217   0.038394   2.584 0.009872 ** 
## covidvaccine_1                   0.190796   0.036754   5.191 2.43e-07 ***
## factor(tr_pid)1:factor(tr_vax)1  0.019416   0.043659   0.445 0.656598    
## factor(tr_pid)2:factor(tr_vax)1 -0.046927   0.043503  -1.079 0.280924    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3155 on 1277 degrees of freedom
##   (156 observations deleted due to missingness)
## Multiple R-squared:  0.256,  Adjusted R-squared:  0.2502 
## F-statistic: 43.95 on 10 and 1277 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod1_1_novax &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df, vaxstatus2 == 0))
summary(mod1_1_novax)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df, vaxstatus2 == 0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.51357 -0.26676 -0.04581  0.25109  0.77940 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.298440   0.048260   6.184 1.96e-09 ***
## factor(tr_pid)1                  0.073460   0.060585   1.213    0.226    
## factor(tr_pid)2                  0.063308   0.056853   1.114    0.266    
## factor(tr_vax)1                  0.143523   0.058102   2.470    0.014 *  
## pid                             -0.011120   0.008347  -1.332    0.184    
## factor(tr_pid)1:factor(tr_vax)1  0.042630   0.090166   0.473    0.637    
## factor(tr_pid)2:factor(tr_vax)1 -0.024395   0.088300  -0.276    0.783    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3205 on 312 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.07881,    Adjusted R-squared:  0.06109 
## F-statistic: 4.449 on 6 and 312 DF,  p-value: 0.0002452</code></pre>
<pre class="r"><code>mod1_1_vax &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df, vaxstatus2 == 1))
summary(mod1_1_vax)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df, vaxstatus2 == 1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.78936 -0.29007  0.04589  0.24671  0.69157 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.493662   0.024366  20.260  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.042624   0.033231  -1.283    0.200    
## factor(tr_pid)2                  0.047488   0.034211   1.388    0.165    
## factor(tr_vax)1                  0.312309   0.033386   9.354  &lt; 2e-16 ***
## pid                             -0.020373   0.004409  -4.621 4.34e-06 ***
## factor(tr_pid)1:factor(tr_vax)1  0.022452   0.051572   0.435    0.663    
## factor(tr_pid)2:factor(tr_vax)1 -0.043730   0.051738  -0.845    0.398    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3252 on 964 degrees of freedom
##   (18 observations deleted due to missingness)
## Multiple R-squared:  0.1955, Adjusted R-squared:  0.1905 
## F-statistic: 39.05 on 6 and 964 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod1_1_nocov &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df, coviddeath == 0))
summary(mod1_1_nocov)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df, coviddeath == 0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.70925 -0.27628 -0.00522  0.28267  0.77159 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.463236   0.025414  18.228  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.038706   0.034097  -1.135    0.257    
## factor(tr_pid)2                  0.009164   0.034965   0.262    0.793    
## factor(tr_vax)1                  0.282110   0.033719   8.367  &lt; 2e-16 ***
## pid                             -0.028018   0.004600  -6.091  1.6e-09 ***
## factor(tr_pid)1:factor(tr_vax)1  0.030624   0.051864   0.590    0.555    
## factor(tr_pid)2:factor(tr_vax)1 -0.013245   0.052948  -0.250    0.803    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3334 on 986 degrees of freedom
##   (17 observations deleted due to missingness)
## Multiple R-squared:  0.1767, Adjusted R-squared:  0.1717 
## F-statistic: 35.28 on 6 and 986 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod1_1_cov &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df, coviddeath == 1))
summary(mod1_1_cov)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df, coviddeath == 1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.76843 -0.23930  0.03917  0.27029  0.61101 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.476550   0.043419  10.976  &lt; 2e-16 ***
## factor(tr_pid)1                  0.071145   0.059477   1.196   0.2325    
## factor(tr_pid)2                  0.096651   0.055293   1.748   0.0814 .  
## factor(tr_vax)1                  0.249296   0.057748   4.317 2.08e-05 ***
## pid                             -0.012509   0.007274  -1.720   0.0864 .  
## factor(tr_pid)1:factor(tr_vax)1 -0.003541   0.091671  -0.039   0.9692    
## factor(tr_pid)2:factor(tr_vax)1 -0.067767   0.084236  -0.804   0.4217    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3272 on 336 degrees of freedom
##   (7 observations deleted due to missingness)
## Multiple R-squared:  0.1314, Adjusted R-squared:  0.1159 
## F-statistic: 8.471 on 6 and 336 DF,  p-value: 1.415e-08</code></pre>
<pre class="r"><code># study II
mod_vax2 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax) + pid + vaxstatus + coviddeath + covidconcern + mandate, data = df2, weights = teamweight)
summary(mod_vax2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid + vaxstatus + coviddeath + covidconcern + mandate, data = df2, 
##     weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.79493 -0.14838  0.03216  0.16492  0.89686 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.20909    0.03858   5.419 7.62e-08 ***
## factor(tr_pid)1                 -0.05012    0.03176  -1.578  0.11491    
## factor(tr_pid)2                  0.03177    0.03049   1.042  0.29771    
## factor(tr_vax)1                  0.19589    0.03174   6.172 1.01e-09 ***
## pid                             -0.01572    0.00500  -3.143  0.00172 ** 
## vaxstatus                        0.07471    0.02386   3.131  0.00179 ** 
## coviddeath                       0.02740    0.02149   1.275  0.20264    
## covidconcern                     0.09720    0.04422   2.198  0.02818 *  
## mandate                          0.23375    0.04069   5.745 1.24e-08 ***
## factor(tr_pid)1:factor(tr_vax)1  0.07191    0.04537   1.585  0.11329    
## factor(tr_pid)2:factor(tr_vax)1  0.07666    0.04529   1.693  0.09085 .  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2805 on 933 degrees of freedom
##   (56 observations deleted due to missingness)
## Multiple R-squared:  0.3451, Adjusted R-squared:  0.3381 
## F-statistic: 49.18 on 10 and 933 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod1_1_novax2 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df2, vaxstatus == 0), weights = teamweight)
summary(mod1_1_novax2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df2, vaxstatus == 0), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.95381 -0.22637  0.01353  0.21353  0.86397 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.46911    0.06811   6.887 6.23e-11 ***
## factor(tr_pid)1                 -0.02193    0.06384  -0.344    0.732    
## factor(tr_pid)2                 -0.03188    0.06397  -0.498    0.619    
## factor(tr_vax)1                 -0.08247    0.06830  -1.207    0.229    
## pid                             -0.02613    0.01019  -2.565    0.011 *  
## factor(tr_pid)1:factor(tr_vax)1  0.13340    0.09160   1.456    0.147    
## factor(tr_pid)2:factor(tr_vax)1  0.23915    0.10032   2.384    0.018 *  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3046 on 214 degrees of freedom
##   (23 observations deleted due to missingness)
## Multiple R-squared:  0.06015,    Adjusted R-squared:  0.0338 
## F-statistic: 2.283 on 6 and 214 DF,  p-value: 0.03713</code></pre>
<pre class="r"><code>mod1_1_vax2 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df2, vaxstatus == 1), weights = teamweight)
summary(mod1_1_vax2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df2, vaxstatus == 1), weights = teamweight)
## 
## Weighted Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0158 -0.1448  0.0362  0.1708  1.1810 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.553186   0.030060  18.403  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.048363   0.037369  -1.294   0.1960    
## factor(tr_pid)2                  0.089923   0.035219   2.553   0.0109 *  
## factor(tr_vax)1                  0.324572   0.036747   8.833  &lt; 2e-16 ***
## pid                             -0.043495   0.005185  -8.389 2.59e-16 ***
## factor(tr_pid)1:factor(tr_vax)1  0.044102   0.053913   0.818   0.4136    
## factor(tr_pid)2:factor(tr_vax)1 -0.025384   0.051971  -0.488   0.6254    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2797 on 719 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.3075, Adjusted R-squared:  0.3018 
## F-statistic: 53.22 on 6 and 719 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod1_1_nocov2 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df2, coviddeath == 0), weights = teamweight)
summary(mod1_1_nocov2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df2, coviddeath == 0), weights = teamweight)
## 
## Weighted Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8440 -0.1840  0.0402  0.1999  1.1684 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.564591   0.034352  16.435  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.058702   0.040884  -1.436   0.1515    
## factor(tr_pid)2                  0.041576   0.039326   1.057   0.2908    
## factor(tr_vax)1                  0.177078   0.038531   4.596 5.14e-06 ***
## pid                             -0.044690   0.005657  -7.899 1.13e-14 ***
## factor(tr_pid)1:factor(tr_vax)1  0.041116   0.057621   0.714   0.4757    
## factor(tr_pid)2:factor(tr_vax)1  0.122139   0.056916   2.146   0.0322 *  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.309 on 678 degrees of freedom
##   (39 observations deleted due to missingness)
## Multiple R-squared:  0.2204, Adjusted R-squared:  0.2135 
## F-statistic: 31.95 on 6 and 678 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod1_1_cov2 &lt;- lm(study1_dv_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df2, coviddeath == 1), weights = teamweight)
summary(mod1_1_cov2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df2, coviddeath == 1), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.84186 -0.14870  0.05073  0.19190  0.76603 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.547512   0.054310  10.081  &lt; 2e-16 ***
## factor(tr_pid)1                  0.007454   0.061833   0.121    0.904    
## factor(tr_pid)2                  0.097855   0.059630   1.641    0.102    
## factor(tr_vax)1                  0.352829   0.075713   4.660 5.08e-06 ***
## pid                             -0.048405   0.008534  -5.672 3.79e-08 ***
## factor(tr_pid)1:factor(tr_vax)1  0.021800   0.096327   0.226    0.821    
## factor(tr_pid)2:factor(tr_vax)1 -0.157817   0.097339  -1.621    0.106    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2855 on 257 degrees of freedom
##   (12 observations deleted due to missingness)
## Multiple R-squared:  0.292,  Adjusted R-squared:  0.2755 
## F-statistic: 17.67 on 6 and 257 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code># creates table 6
modelsummary(models = list(&#39;Study I&#39;  = mod_vax,
                           &#39;Study II&#39; = mod_vax2,
                           &#39;Study I&#39;  = mod1_1_novax,
                           &#39;Study II&#39; = mod1_1_novax2,
                           &#39;Study I&#39;  = mod1_1_vax,
                           &#39;Study II&#39; = mod1_1_vax2,
                           &#39;Study I&#39;  = mod1_1_nocov,
                           &#39;Study II&#39; = mod1_1_nocov2,
                           &#39;Study I&#39;  = mod1_1_cov,
                           &#39;Study II&#39; = mod1_1_cov2),
             estimate = &#39;{estimate}{stars} ({std.error})&#39;,
             statistic = &#39;[{p.value}]&#39;,
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code># models for table 7
# study I
mod2_vax &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax) + pid + vaxstatus2 + coviddeath + covidconcern_1 + covidvaccine_1, data = df)
summary(mod2_vax)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid + vaxstatus2 + coviddeath + covidconcern_1 + covidvaccine_1, 
##     data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7627 -0.2025  0.0408  0.2371  0.5782 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.641977   0.033356  19.246  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.002642   0.026730  -0.099   0.9213    
## factor(tr_pid)2                 -0.104913   0.026838  -3.909 9.75e-05 ***
## factor(tr_vax)1                 -0.185975   0.026559  -7.002 4.06e-12 ***
## pid                              0.005419   0.003786   1.431   0.1526    
## vaxstatus2                      -0.036656   0.021425  -1.711   0.0873 .  
## coviddeath                       0.078606   0.019003   4.137 3.76e-05 ***
## covidconcern_1                   0.146491   0.036053   4.063 5.14e-05 ***
## covidvaccine_1                  -0.042416   0.034486  -1.230   0.2189    
## factor(tr_pid)1:factor(tr_vax)1 -0.001102   0.041012  -0.027   0.9786    
## factor(tr_pid)2:factor(tr_vax)1  0.066269   0.040834   1.623   0.1049    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2962 on 1278 degrees of freedom
##   (155 observations deleted due to missingness)
## Multiple R-squared:  0.1187, Adjusted R-squared:  0.1118 
## F-statistic: 17.22 on 10 and 1278 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod2_1_novax &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df, vaxstatus2 == 0))
summary(mod1_1_novax)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df, vaxstatus2 == 0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.51357 -0.26676 -0.04581  0.25109  0.77940 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.298440   0.048260   6.184 1.96e-09 ***
## factor(tr_pid)1                  0.073460   0.060585   1.213    0.226    
## factor(tr_pid)2                  0.063308   0.056853   1.114    0.266    
## factor(tr_vax)1                  0.143523   0.058102   2.470    0.014 *  
## pid                             -0.011120   0.008347  -1.332    0.184    
## factor(tr_pid)1:factor(tr_vax)1  0.042630   0.090166   0.473    0.637    
## factor(tr_pid)2:factor(tr_vax)1 -0.024395   0.088300  -0.276    0.783    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3205 on 312 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.07881,    Adjusted R-squared:  0.06109 
## F-statistic: 4.449 on 6 and 312 DF,  p-value: 0.0002452</code></pre>
<pre class="r"><code>mod2_1_vax &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df, vaxstatus2 == 1))
summary(mod1_1_vax)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df, vaxstatus2 == 1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.78936 -0.29007  0.04589  0.24671  0.69157 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.493662   0.024366  20.260  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.042624   0.033231  -1.283    0.200    
## factor(tr_pid)2                  0.047488   0.034211   1.388    0.165    
## factor(tr_vax)1                  0.312309   0.033386   9.354  &lt; 2e-16 ***
## pid                             -0.020373   0.004409  -4.621 4.34e-06 ***
## factor(tr_pid)1:factor(tr_vax)1  0.022452   0.051572   0.435    0.663    
## factor(tr_pid)2:factor(tr_vax)1 -0.043730   0.051738  -0.845    0.398    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3252 on 964 degrees of freedom
##   (18 observations deleted due to missingness)
## Multiple R-squared:  0.1955, Adjusted R-squared:  0.1905 
## F-statistic: 39.05 on 6 and 964 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod2_1_nocov &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df, coviddeath == 0))
summary(mod1_1_nocov)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df, coviddeath == 0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.70925 -0.27628 -0.00522  0.28267  0.77159 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.463236   0.025414  18.228  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.038706   0.034097  -1.135    0.257    
## factor(tr_pid)2                  0.009164   0.034965   0.262    0.793    
## factor(tr_vax)1                  0.282110   0.033719   8.367  &lt; 2e-16 ***
## pid                             -0.028018   0.004600  -6.091  1.6e-09 ***
## factor(tr_pid)1:factor(tr_vax)1  0.030624   0.051864   0.590    0.555    
## factor(tr_pid)2:factor(tr_vax)1 -0.013245   0.052948  -0.250    0.803    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3334 on 986 degrees of freedom
##   (17 observations deleted due to missingness)
## Multiple R-squared:  0.1767, Adjusted R-squared:  0.1717 
## F-statistic: 35.28 on 6 and 986 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod2_1_cov &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df, coviddeath == 1))
summary(mod2_1_cov)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df, coviddeath == 1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.66915 -0.17220  0.06185  0.21821  0.42352 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.760143   0.036979  20.556  &lt; 2e-16 ***
## factor(tr_pid)1                  0.019584   0.050723   0.386  0.69966    
## factor(tr_pid)2                 -0.097163   0.047156  -2.060  0.04012 *  
## factor(tr_vax)1                 -0.174053   0.048943  -3.556  0.00043 ***
## pid                              0.002058   0.006182   0.333  0.73938    
## factor(tr_pid)1:factor(tr_vax)1  0.057612   0.077762   0.741  0.45928    
## factor(tr_pid)2:factor(tr_vax)1  0.085492   0.071630   1.194  0.23351    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2791 on 338 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.07651,    Adjusted R-squared:  0.06012 
## F-statistic: 4.667 on 6 and 338 DF,  p-value: 0.0001411</code></pre>
<pre class="r"><code># study II
mod2_vax2 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax) + pid + vaxstatus + coviddeath + covidconcern + mandate, data = df2, weights = teamweight)
summary(mod2_vax2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid + vaxstatus + coviddeath + covidconcern + mandate, data = df2, 
##     weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.51896 -0.18470  0.00569  0.18917  1.43221 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.749373   0.039705  18.873  &lt; 2e-16 ***
## factor(tr_pid)1                  0.047290   0.032733   1.445   0.1489    
## factor(tr_pid)2                 -0.046788   0.031420  -1.489   0.1368    
## factor(tr_vax)1                 -0.134059   0.032658  -4.105 4.40e-05 ***
## pid                              0.008168   0.005144   1.588   0.1126    
## vaxstatus                       -0.051279   0.024487  -2.094   0.0365 *  
## coviddeath                      -0.012004   0.022159  -0.542   0.5881    
## covidconcern                     0.026663   0.045577   0.585   0.5587    
## mandate                         -0.180002   0.041936  -4.292 1.95e-05 ***
## factor(tr_pid)1:factor(tr_vax)1 -0.063447   0.046698  -1.359   0.1746    
## factor(tr_pid)2:factor(tr_vax)1 -0.033709   0.046614  -0.723   0.4698    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2893 on 936 degrees of freedom
##   (53 observations deleted due to missingness)
## Multiple R-squared:  0.1571, Adjusted R-squared:  0.1481 
## F-statistic: 17.44 on 10 and 936 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod2_1_novax2 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df2, vaxstatus == 0), weights = teamweight)
summary(mod2_1_novax2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df2, vaxstatus == 0), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.97553 -0.14931  0.03043  0.18051  0.83374 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.607451   0.060168  10.096   &lt;2e-16 ***
## factor(tr_pid)1                  0.046917   0.056546   0.830    0.408    
## factor(tr_pid)2                 -0.036099   0.056655  -0.637    0.525    
## factor(tr_vax)1                  0.066517   0.060486   1.100    0.273    
## pid                              0.010908   0.008979   1.215    0.226    
## factor(tr_pid)1:factor(tr_vax)1 -0.093855   0.081033  -1.158    0.248    
## factor(tr_pid)2:factor(tr_vax)1 -0.083274   0.088419  -0.942    0.347    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2698 on 216 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.02847,    Adjusted R-squared:  0.001486 
## F-statistic: 1.055 on 6 and 216 DF,  p-value: 0.3908</code></pre>
<pre class="r"><code>mod2_1_vax2 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df2, vaxstatus == 1), weights = teamweight)
summary(mod2_1_vax2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df2, vaxstatus == 1), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.70905 -0.19526  0.01559  0.19006  0.92511 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.573688   0.031575  18.169  &lt; 2e-16 ***
## factor(tr_pid)1                  0.047594   0.039223   1.213   0.2254    
## factor(tr_pid)2                 -0.067373   0.036944  -1.824   0.0686 .  
## factor(tr_vax)1                 -0.223248   0.038488  -5.800 9.90e-09 ***
## pid                              0.023345   0.005432   4.297 1.96e-05 ***
## factor(tr_pid)1:factor(tr_vax)1 -0.054682   0.056580  -0.966   0.3341    
## factor(tr_pid)2:factor(tr_vax)1  0.021752   0.054538   0.399   0.6901    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.294 on 720 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.1551, Adjusted R-squared:  0.1481 
## F-statistic: 22.03 on 6 and 720 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>mod2_1_nocov2 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df2, coviddeath == 0), weights = teamweight)
summary(mod2_1_nocov2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df2, coviddeath == 0), weights = teamweight)
## 
## Weighted Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6299 -0.1804 -0.0048  0.1969  1.4289 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.529860   0.033333  15.896  &lt; 2e-16 ***
## factor(tr_pid)1                  0.075994   0.039658   1.916   0.0558 .  
## factor(tr_pid)2                 -0.029544   0.038129  -0.775   0.4387    
## factor(tr_vax)1                 -0.095057   0.037368  -2.544   0.0112 *  
## pid                              0.025136   0.005465   4.599 5.06e-06 ***
## factor(tr_pid)1:factor(tr_vax)1 -0.056945   0.055916  -1.018   0.3088    
## factor(tr_pid)2:factor(tr_vax)1 -0.049712   0.055215  -0.900   0.3683    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3005 on 681 degrees of freedom
##   (36 observations deleted due to missingness)
## Multiple R-squared:  0.08979,    Adjusted R-squared:  0.08177 
## F-statistic:  11.2 on 6 and 681 DF,  p-value: 6.119e-12</code></pre>
<pre class="r"><code>mod2_1_cov2 &lt;- lm(study1_dv2_1 ~ factor(tr_pid)*factor(tr_vax) + pid, data = subset(df2, coviddeath == 1), weights = teamweight)
summary(mod2_1_cov2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr_pid) * factor(tr_vax) + 
##     pid, data = subset(df2, coviddeath == 1), weights = teamweight)
## 
## Weighted Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0189 -0.1838  0.0161  0.2042  0.6412 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)                      0.671772   0.053855  12.474  &lt; 2e-16 ***
## factor(tr_pid)1                 -0.060965   0.061483  -0.992  0.32234    
## factor(tr_pid)2                 -0.166582   0.059348  -2.807  0.00539 ** 
## factor(tr_vax)1                 -0.302766   0.074461  -4.066 6.36e-05 ***
## pid                              0.023907   0.008477   2.820  0.00517 ** 
## factor(tr_pid)1:factor(tr_vax)1  0.006594   0.094966   0.069  0.94470    
## factor(tr_pid)2:factor(tr_vax)1  0.101868   0.095971   1.061  0.28948    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2826 on 257 degrees of freedom
##   (12 observations deleted due to missingness)
## Multiple R-squared:  0.2079, Adjusted R-squared:  0.1894 
## F-statistic: 11.24 on 6 and 257 DF,  p-value: 3.842e-11</code></pre>
<pre class="r"><code># creates table 7
modelsummary(models = list(&#39;Study I&#39;  = mod2_vax,
                           &#39;Study II&#39; = mod2_vax2,
                           &#39;Study I&#39;  = mod2_1_novax,
                           &#39;Study II&#39; = mod2_1_novax2,
                           &#39;Study I&#39;  = mod2_1_vax,
                           &#39;Study II&#39; = mod2_1_vax2,
                           &#39;Study I&#39;  = mod2_1_nocov,
                           &#39;Study II&#39; = mod2_1_nocov2,
                           &#39;Study I&#39;  = mod2_1_cov,
                           &#39;Study II&#39; = mod2_1_cov2),
             fmt = fmt_decimal(3, 16),
             estimate = &#39;{estimate}{stars} ({std.error})&#39;,
             statistic = &#39;[{p.value}]&#39;,
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>### Supplementary Analysis

# blaming victim

m1_1 &lt;- lm(study1_dv_1 ~ factor(tr1), data = df) # 
summary(m1_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr1), data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.65510 -0.34634  0.04551  0.33366  0.64021 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.37752    0.01709  22.095   &lt;2e-16 ***
## factor(tr1)2  0.27635    0.02952   9.362   &lt;2e-16 ***
## factor(tr1)3 -0.01773    0.02987  -0.593    0.553    
## factor(tr1)4  0.03882    0.02993   1.297    0.195    
## factor(tr1)5  0.27382    0.03014   9.084   &lt;2e-16 ***
## factor(tr1)6  0.27758    0.02967   9.356   &lt;2e-16 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3396 on 1354 degrees of freedom
##   (84 observations deleted due to missingness)
## Multiple R-squared:  0.1368, Adjusted R-squared:  0.1336 
## F-statistic: 42.92 on 5 and 1354 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>m1_dem_1 &lt;- lm(study1_dv_1 ~ factor(tr1), data = subset(df, gop == 0)) # 
summary(m1_dem_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr1), data = subset(df, gop == 
##     0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.73337 -0.25920  0.04663  0.27604  0.60031 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.43929    0.02320  18.937  &lt; 2e-16 ***
## factor(tr1)2  0.28466    0.04134   6.886 1.35e-11 ***
## factor(tr1)3 -0.03960    0.04031  -0.982   0.3263    
## factor(tr1)4  0.08264    0.04103   2.014   0.0444 *  
## factor(tr1)5  0.27964    0.04250   6.579 9.67e-11 ***
## factor(tr1)6  0.29407    0.04031   7.294 8.68e-13 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3264 on 656 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.1573, Adjusted R-squared:  0.1509 
## F-statistic: 24.49 on 5 and 656 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>m1_gop_1 &lt;- lm(study1_dv_1 ~ factor(tr1), data = subset(df, gop == 1)) # 
summary(m1_gop_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr1), data = subset(df, gop == 
##     1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.61260 -0.32042 -0.04161  0.29727  0.67958 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.3333835  0.0297173  11.219  &lt; 2e-16 ***
## factor(tr1)2  0.2397334  0.0490765   4.885 1.41e-06 ***
## factor(tr1)3  0.0006464  0.0513436   0.013    0.990    
## factor(tr1)4 -0.0129609  0.0503727  -0.257    0.797    
## factor(tr1)5  0.2792139  0.0490765   5.689 2.21e-08 ***
## factor(tr1)6  0.2772319  0.0518663   5.345 1.39e-07 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3427 on 484 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1338, Adjusted R-squared:  0.1249 
## F-statistic: 14.95 on 5 and 484 DF,  p-value: 1.162e-13</code></pre>
<pre class="r"><code>m2_1 &lt;- lm(study1_dv_1 ~ factor(trgrp), data = df2, weights = teamweight) # 
summary(m2_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(trgrp), data = df2, weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.48526 -0.19977  0.06772  0.22769  1.15092 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.39501    0.02400  16.460  &lt; 2e-16 ***
## factor(trgrp)2  0.16637    0.03469   4.795 1.88e-06 ***
## factor(trgrp)3 -0.03920    0.03452  -1.136    0.256    
## factor(trgrp)4  0.03065    0.03407   0.899    0.369    
## factor(trgrp)5  0.21605    0.03501   6.171 9.87e-10 ***
## factor(trgrp)6  0.32936    0.03599   9.152  &lt; 2e-16 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3221 on 989 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.1371, Adjusted R-squared:  0.1328 
## F-statistic: 31.44 on 5 and 989 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>m2_dem_1 &lt;- lm(study1_dv_1 ~ factor(trgrp), data = subset(df2, gop == 0), weights = teamweight) # 
summary(m2_dem_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(trgrp), data = subset(df2, 
##     gop == 0), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.84023 -0.12635  0.06495  0.17266  0.71935 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.48259    0.03071  15.713  &lt; 2e-16 ***
## factor(trgrp)2  0.21295    0.04551   4.679 3.84e-06 ***
## factor(trgrp)3 -0.04296    0.04606  -0.933   0.3515    
## factor(trgrp)4  0.13712    0.04670   2.936   0.0035 ** 
## factor(trgrp)5  0.24363    0.04335   5.620 3.39e-08 ***
## factor(trgrp)6  0.40428    0.04478   9.028  &lt; 2e-16 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2714 on 442 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2254, Adjusted R-squared:  0.2167 
## F-statistic: 25.73 on 5 and 442 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>m2_gop_1 &lt;- lm(study1_dv_1 ~ factor(trgrp), data = subset(df2, gop == 1), weights = teamweight) # 
summary(m2_gop_1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(trgrp), data = subset(df2, 
##     gop == 1), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.76838 -0.25111 -0.00589  0.25055  0.99664 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.32306    0.04348   7.431 1.06e-12 ***
## factor(trgrp)2  0.18327    0.06273   2.922  0.00374 ** 
## factor(trgrp)3 -0.02922    0.06366  -0.459  0.64651    
## factor(trgrp)4 -0.02417    0.05873  -0.412  0.68097    
## factor(trgrp)5  0.16614    0.06490   2.560  0.01094 *  
## factor(trgrp)6  0.17675    0.06414   2.756  0.00620 ** 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3293 on 310 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.08342,    Adjusted R-squared:  0.06864 
## F-statistic: 5.643 on 5 and 310 DF,  p-value: 5.376e-05</code></pre>
<pre class="r"><code># creates table F.1 
modelsummary(models = list(&#39;Full Sample&#39;  = m1_1,
                           &#39;Democrats&#39; = m1_dem_1,
                           &#39;Republicans&#39; = m1_gop_1),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>#creates table F.2
modelsummary(models = list(&#39;Full Sample&#39;  = m2_1,
                           &#39;Democrats&#39; = m2_dem_1,
                           &#39;Republicans&#39; = m2_gop_1),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code># blaming govt

m1_2 &lt;- lm(study1_dv_2 ~ factor(tr1), data = df) # 
summary(m1_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_2 ~ factor(tr1), data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.38982 -0.34809 -0.02982  0.26018  0.65191 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.389823   0.017295  22.540   &lt;2e-16 ***
## factor(tr1)2 -0.041733   0.029905  -1.396    0.163    
## factor(tr1)3 -0.014198   0.030266  -0.469    0.639    
## factor(tr1)4 -0.023802   0.030319  -0.785    0.433    
## factor(tr1)5 -0.005361   0.030593  -0.175    0.861    
## factor(tr1)6 -0.006252   0.030057  -0.208    0.835    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3442 on 1354 degrees of freedom
##   (84 observations deleted due to missingness)
## Multiple R-squared:  0.001703,   Adjusted R-squared:  -0.001984 
## F-statistic: 0.4619 on 5 and 1354 DF,  p-value: 0.8048</code></pre>
<pre class="r"><code>m1_dem_2 &lt;- lm(study1_dv_2 ~ factor(tr1), data = subset(df, gop == 0)) # 
summary(m1_dem_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_2 ~ factor(tr1), data = subset(df, gop == 
##     0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.46462 -0.37512  0.03488  0.29488  0.61488 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.431616   0.024752  17.438   &lt;2e-16 ***
## factor(tr1)2  0.032999   0.044110   0.748    0.455    
## factor(tr1)3  0.007159   0.043017   0.166    0.868    
## factor(tr1)4 -0.021939   0.043784  -0.501    0.616    
## factor(tr1)5 -0.046497   0.045352  -1.025    0.306    
## factor(tr1)6  0.010935   0.043017   0.254    0.799    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3483 on 656 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.004256,   Adjusted R-squared:  -0.003333 
## F-statistic: 0.5608 on 5 and 656 DF,  p-value: 0.7301</code></pre>
<pre class="r"><code>m1_gop_2 &lt;- lm(study1_dv_2 ~ factor(tr1), data = subset(df, gop == 1)) # 
summary(m1_gop_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_2 ~ factor(tr1), data = subset(df, gop == 
##     1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.39684 -0.32478 -0.08985  0.26468  0.67522 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.344925   0.029082  11.860   &lt;2e-16 ***
## factor(tr1)2 -0.099601   0.048142  -2.069   0.0391 *  
## factor(tr1)3 -0.020149   0.050372  -0.400   0.6893    
## factor(tr1)4 -0.012531   0.049417  -0.254   0.7999    
## factor(tr1)5  0.051917   0.048343   1.074   0.2834    
## factor(tr1)6  0.006921   0.050886   0.136   0.8919    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3367 on 484 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.01699,    Adjusted R-squared:  0.006836 
## F-statistic: 1.673 on 5 and 484 DF,  p-value: 0.1395</code></pre>
<pre class="r"><code>m2_2 &lt;- lm(study1_dv_2 ~ factor(trgrp), data = df2) # 
summary(m2_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_2 ~ factor(trgrp), data = df2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.34645 -0.26867 -0.07407  0.20059  0.72442 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.34645    0.02282  15.183   &lt;2e-16 ***
## factor(trgrp)2 -0.03002    0.03232  -0.929   0.3532    
## factor(trgrp)3 -0.01704    0.03213  -0.530   0.5960    
## factor(trgrp)4 -0.03237    0.03222  -1.005   0.3153    
## factor(trgrp)5 -0.07086    0.03242  -2.186   0.0291 *  
## factor(trgrp)6 -0.06778    0.03232  -2.097   0.0362 *  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.294 on 989 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.007532,   Adjusted R-squared:  0.002514 
## F-statistic: 1.501 on 5 and 989 DF,  p-value: 0.1868</code></pre>
<pre class="r"><code>m2_dem_2 &lt;- lm(study1_dv_2 ~ factor(trgrp), data = subset(df2, gop == 0)) # 
summary(m2_dem_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_2 ~ factor(trgrp), data = subset(df2, 
##     gop == 0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.36707 -0.23267 -0.05707  0.16723  0.77430 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.35527    0.03213  11.057  &lt; 2e-16 ***
## factor(trgrp)2 -0.07971    0.04575  -1.742  0.08216 .  
## factor(trgrp)3  0.01180    0.04432   0.266  0.79011    
## factor(trgrp)4 -0.02250    0.04699  -0.479  0.63225    
## factor(trgrp)5 -0.07260    0.04529  -1.603  0.10961    
## factor(trgrp)6 -0.12957    0.04471  -2.898  0.00395 ** 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2764 on 441 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.03298,    Adjusted R-squared:  0.02202 
## F-statistic: 3.008 on 5 and 441 DF,  p-value: 0.01107</code></pre>
<pre class="r"><code>m2_gop_2 &lt;- lm(study1_dv_2 ~ factor(trgrp), data = subset(df2, gop == 1)) # 
summary(m2_gop_2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_2 ~ factor(trgrp), data = subset(df2, 
##     gop == 1))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.3491 -0.2454 -0.1140  0.2268  0.7900 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.271200   0.041468   6.540 2.51e-10 ***
## factor(trgrp)2  0.009569   0.058077   0.165    0.869    
## factor(trgrp)3  0.017930   0.059905   0.299    0.765    
## factor(trgrp)4  0.055625   0.055536   1.002    0.317    
## factor(trgrp)5 -0.061200   0.058644  -1.044    0.297    
## factor(trgrp)6  0.077923   0.056815   1.372    0.171    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2932 on 312 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.02283,    Adjusted R-squared:  0.007168 
## F-statistic: 1.458 on 5 and 312 DF,  p-value: 0.2035</code></pre>
<pre class="r"><code># blaming activists

m1_3 &lt;- lm(study1_dv_3 ~ factor(tr1), data = df) # 
summary(m1_3)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_3 ~ factor(tr1), data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.46797 -0.33110 -0.01514  0.28047  0.66911 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.37514    0.01723  21.773   &lt;2e-16 ***
## factor(tr1)2  0.04672    0.02977   1.570   0.1168    
## factor(tr1)3 -0.04425    0.03018  -1.466   0.1428    
## factor(tr1)4 -0.02404    0.03018  -0.797   0.4258    
## factor(tr1)5  0.09283    0.03040   3.054   0.0023 ** 
## factor(tr1)6  0.05981    0.02992   1.999   0.0458 *  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3424 on 1353 degrees of freedom
##   (85 observations deleted due to missingness)
## Multiple R-squared:  0.01709,    Adjusted R-squared:  0.01346 
## F-statistic: 4.706 on 5 and 1353 DF,  p-value: 0.000289</code></pre>
<pre class="r"><code>m1_dem_3 &lt;- lm(study1_dv_3 ~ factor(tr1), data = subset(df, gop == 0)) # 
summary(m1_dem_3)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_3 ~ factor(tr1), data = subset(df, gop == 
##     0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.53923 -0.34024  0.01422  0.27440  0.62939 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.44616    0.02399  18.595   &lt;2e-16 ***
## factor(tr1)2  0.09307    0.04276   2.177   0.0299 *  
## factor(tr1)3 -0.07555    0.04170  -1.812   0.0705 .  
## factor(tr1)4 -0.01702    0.04244  -0.401   0.6885    
## factor(tr1)5  0.07836    0.04396   1.782   0.0751 .  
## factor(tr1)6  0.05925    0.04170   1.421   0.1559    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3376 on 656 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.02632,    Adjusted R-squared:  0.0189 
## F-statistic: 3.547 on 5 and 656 DF,  p-value: 0.003584</code></pre>
<pre class="r"><code>m1_gop_3 &lt;- lm(study1_dv_3 ~ factor(tr1), data = subset(df, gop == 1)) # 
summary(m1_gop_3)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_3 ~ factor(tr1), data = subset(df, gop == 
##     1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.44130 -0.30312 -0.07312  0.25870  0.71268 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.310226   0.029317  10.582  &lt; 2e-16 ***
## factor(tr1)2 -0.007109   0.048415  -0.147  0.88333    
## factor(tr1)3  0.006789   0.050652   0.134  0.89343    
## factor(tr1)4 -0.022902   0.049694  -0.461  0.64511    
## factor(tr1)5  0.131073   0.048415   2.707  0.00702 ** 
## factor(tr1)6  0.068236   0.051167   1.334  0.18297    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3381 on 484 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.02356,    Adjusted R-squared:  0.01347 
## F-statistic: 2.336 on 5 and 484 DF,  p-value: 0.0411</code></pre>
<pre class="r"><code>m2_3 &lt;- lm(study1_dv_3 ~ factor(trgrp), data = df2) # 
summary(m2_3)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_3 ~ factor(trgrp), data = df2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.44715 -0.29393  0.03285  0.22131  0.66042 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.3395758  0.0242320  14.014  &lt; 2e-16 ***
## factor(trgrp)2  0.1036170  0.0342176   3.028  0.00252 ** 
## factor(trgrp)3 -0.0458853  0.0341159  -1.345  0.17894    
## factor(trgrp)4  0.0006623  0.0341159   0.019  0.98451    
## factor(trgrp)5  0.0545960  0.0343742   1.588  0.11254    
## factor(trgrp)6  0.1075758  0.0342692   3.139  0.00174 ** 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3113 on 989 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.03242,    Adjusted R-squared:  0.02753 
## F-statistic: 6.627 on 5 and 989 DF,  p-value: 4.429e-06</code></pre>
<pre class="r"><code>m2_dem_3 &lt;- lm(study1_dv_3 ~ factor(trgrp), data = subset(df2, gop == 0)) # 
summary(m2_dem_3)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_3 ~ factor(trgrp), data = subset(df2, 
##     gop == 0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.56620 -0.29458  0.03307  0.22185  0.64311 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.35689    0.03564  10.014  &lt; 2e-16 ***
## factor(trgrp)2  0.20931    0.05093   4.109 4.73e-05 ***
## factor(trgrp)3 -0.02924    0.04930  -0.593 0.553473    
## factor(trgrp)4  0.05477    0.05191   1.055 0.291907    
## factor(trgrp)5  0.09631    0.05024   1.917 0.055869 .  
## factor(trgrp)6  0.18273    0.04960   3.684 0.000258 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3066 on 440 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.07829,    Adjusted R-squared:  0.06782 
## F-statistic: 7.475 on 5 and 440 DF,  p-value: 9.532e-07</code></pre>
<pre class="r"><code>m2_gop_3 &lt;- lm(study1_dv_3 ~ factor(trgrp), data = subset(df2, gop == 1)) # 
summary(m2_gop_3)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_3 ~ factor(trgrp), data = subset(df2, 
##     gop == 1))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.3330 -0.2490 -0.1098  0.2068  0.7309 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.29569    0.04115   7.186 4.88e-12 ***
## factor(trgrp)2  0.02280    0.05764   0.396    0.693    
## factor(trgrp)3 -0.04677    0.05975  -0.783    0.434    
## factor(trgrp)4 -0.03664    0.05535  -0.662    0.508    
## factor(trgrp)5 -0.02509    0.05848  -0.429    0.668    
## factor(trgrp)6  0.03730    0.05664   0.659    0.511    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2938 on 314 degrees of freedom
## Multiple R-squared:  0.01115,    Adjusted R-squared:  -0.004592 
## F-statistic: 0.7084 on 5 and 314 DF,  p-value: 0.6175</code></pre>
<pre class="r"><code># sympathy

m1_4 &lt;- lm(study1_dv2_1 ~ factor(tr1), data = df) # 
summary(m1_4)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr1), data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.72288 -0.22091  0.02909  0.27408  0.50338 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.720907   0.015072  47.832  &lt; 2e-16 ***
## factor(tr1)2 -0.186357   0.026039  -7.157 1.35e-12 ***
## factor(tr1)3  0.001973   0.026444   0.075 0.940543    
## factor(tr1)4 -0.095357   0.026444  -3.606 0.000322 ***
## factor(tr1)5 -0.185215   0.026586  -6.967 5.05e-12 ***
## factor(tr1)6 -0.224291   0.026260  -8.541  &lt; 2e-16 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3003 on 1356 degrees of freedom
##   (82 observations deleted due to missingness)
## Multiple R-squared:  0.08817,    Adjusted R-squared:  0.08481 
## F-statistic: 26.22 on 5 and 1356 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>m1_dem_4 &lt;- lm(study1_dv2_1 ~ factor(tr1), data = subset(df, gop == 0)) # 
summary(m1_dem_4)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr1), data = subset(df, gop == 
##     0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.78536 -0.21878  0.02857  0.22735  0.54439 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.73613    0.02118  34.755  &lt; 2e-16 ***
## factor(tr1)2 -0.20559    0.03767  -5.458 6.84e-08 ***
## factor(tr1)3  0.04923    0.03700   1.331    0.184    
## factor(tr1)4 -0.18226    0.03753  -4.856 1.50e-06 ***
## factor(tr1)5 -0.18470    0.03888  -4.751 2.49e-06 ***
## factor(tr1)6 -0.28052    0.03687  -7.608 9.70e-14 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2988 on 657 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1395, Adjusted R-squared:  0.133 
## F-statistic:  21.3 on 5 and 657 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>m1_gop_4 &lt;- lm(study1_dv2_1 ~ factor(tr1), data = subset(df, gop == 1)) # 
summary(m1_gop_4)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr1), data = subset(df, gop == 
##     1))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7606 -0.2215  0.0194  0.2394  0.4574 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.76060    0.02474  30.743  &lt; 2e-16 ***
## factor(tr1)2 -0.21358    0.04095  -5.215 2.72e-07 ***
## factor(tr1)3 -0.12284    0.04285  -2.867  0.00433 ** 
## factor(tr1)4 -0.05820    0.04204  -1.384  0.16684    
## factor(tr1)5 -0.21803    0.04079  -5.346 1.39e-07 ***
## factor(tr1)6 -0.18075    0.04352  -4.154 3.87e-05 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2864 on 485 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.08838,    Adjusted R-squared:  0.07899 
## F-statistic: 9.404 on 5 and 485 DF,  p-value: 1.439e-08</code></pre>
<pre class="r"><code>m2_4 &lt;- lm(study1_dv2_1 ~ factor(trgrp), data = df2, weights = teamweight) # 
summary(m2_4)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(trgrp), data = df2, weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.56856 -0.19407  0.01433  0.19875  1.31409 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.65508    0.02258  29.007  &lt; 2e-16 ***
## factor(trgrp)2 -0.15176    0.03268  -4.643 3.89e-06 ***
## factor(trgrp)3  0.02571    0.03279   0.784   0.4332    
## factor(trgrp)4 -0.05295    0.03211  -1.649   0.0995 .  
## factor(trgrp)5 -0.15461    0.03295  -4.692 3.09e-06 ***
## factor(trgrp)6 -0.22472    0.03388  -6.632 5.44e-11 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.304 on 990 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.0788, Adjusted R-squared:  0.07415 
## F-statistic: 16.94 on 5 and 990 DF,  p-value: 4.32e-16</code></pre>
<pre class="r"><code>m2_dem_4 &lt;- lm(study1_dv2_1 ~ factor(trgrp), data = subset(df2, gop == 0), weights = teamweight) # 
summary(m2_dem_4)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(trgrp), data = subset(df2, 
##     gop == 0), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.86496 -0.21579 -0.00948  0.15449  1.49796 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.718508   0.032035  22.429  &lt; 2e-16 ***
## factor(trgrp)2 -0.284682   0.047338  -6.014 3.80e-09 ***
## factor(trgrp)3 -0.002313   0.048046  -0.048    0.962    
## factor(trgrp)4 -0.234117   0.048711  -4.806 2.11e-06 ***
## factor(trgrp)5 -0.297218   0.045220  -6.573 1.40e-10 ***
## factor(trgrp)6 -0.415311   0.046888  -8.858  &lt; 2e-16 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2831 on 442 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2204, Adjusted R-squared:  0.2115 
## F-statistic: 24.99 on 5 and 442 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>m2_gop_4 &lt;- lm(study1_dv2_1 ~ factor(trgrp), data = subset(df2, gop == 1), weights = teamweight) # 
summary(m2_gop_4)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(trgrp), data = subset(df2, 
##     gop == 1), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.50407 -0.14856  0.03336  0.22877  0.58626 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.628149   0.038456  16.334   &lt;2e-16 ***
## factor(trgrp)2 -0.002086   0.055744  -0.037   0.9702    
## factor(trgrp)3  0.118055   0.056578   2.087   0.0377 *  
## factor(trgrp)4  0.059301   0.052162   1.137   0.2565    
## factor(trgrp)5 -0.022947   0.057434  -0.400   0.6898    
## factor(trgrp)6 -0.048629   0.056522  -0.860   0.3903    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2939 on 313 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.03441,    Adjusted R-squared:  0.01898 
## F-statistic: 2.231 on 5 and 313 DF,  p-value: 0.05115</code></pre>
<pre class="r"><code># creates table F.3
modelsummary(models = list(&#39;Full Sample&#39;  = m1_4,
                           &#39;Democrats&#39; = m1_dem_4,
                           &#39;Republicans&#39; = m1_gop_4),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code># creates table F.4
modelsummary(models = list(&#39;Full Sample&#39;  = m2_4,
                           &#39;Democrats&#39; = m2_dem_4,
                           &#39;Republicans&#39; = m2_gop_4),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code># Testing Group Empathy Theory
# analysis in SI, section G
## Blame

em_death1.1 &lt;- lm(study1_dv_1 ~ factor(tr1), data = subset(df, coviddeath == 1))
summary(em_death1.1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr1), data = subset(df, coviddeath == 
##     1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.74341 -0.25621  0.04724  0.28862  0.57937 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.42063    0.03374  12.467  &lt; 2e-16 ***
## factor(tr1)2  0.26386    0.05804   4.546 7.59e-06 ***
## factor(tr1)3  0.06597    0.05885   1.121   0.2631    
## factor(tr1)4  0.10148    0.05528   1.836   0.0673 .  
## factor(tr1)5  0.32279    0.06168   5.234 2.91e-07 ***
## factor(tr1)6  0.29075    0.05498   5.289 2.20e-07 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3306 on 342 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.131,  Adjusted R-squared:  0.1183 
## F-statistic: 10.31 on 5 and 342 DF,  p-value: 3.19e-09</code></pre>
<pre class="r"><code>em_nodeath1.1 &lt;- lm(study1_dv_1 ~ factor(tr1), data = subset(df, coviddeath == 0))
summary(em_nodeath1.1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr1), data = subset(df, coviddeath == 
##     0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.64148 -0.31811 -0.00443  0.32197  0.68189 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.364430   0.019668  18.529  &lt; 2e-16 ***
## factor(tr1)2  0.277047   0.034066   8.133 1.23e-15 ***
## factor(tr1)3 -0.046318   0.034539  -1.341    0.180    
## factor(tr1)4  0.006914   0.035314   0.196    0.845    
## factor(tr1)5  0.261050   0.034298   7.611 6.26e-14 ***
## factor(tr1)6  0.266154   0.035046   7.594 7.08e-14 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3395 on 1001 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.1436, Adjusted R-squared:  0.1394 
## F-statistic: 33.58 on 5 and 1001 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>em_death2.1 &lt;- lm(study1_dv_1 ~ factor(trgrp), data = subset(df2, coviddeath == 1), weights = teamweight)
summary(em_death2.1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(trgrp), data = subset(df2, 
##     coviddeath == 1), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.02579 -0.17930  0.08592  0.21780  0.78532 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.394696   0.047413   8.325 4.32e-15 ***
## factor(trgrp)2 0.256524   0.077835   3.296  0.00111 ** 
## factor(trgrp)3 0.002705   0.062380   0.043  0.96544    
## factor(trgrp)4 0.057694   0.062999   0.916  0.36060    
## factor(trgrp)5 0.333795   0.065406   5.103 6.31e-07 ***
## factor(trgrp)6 0.257104   0.070522   3.646  0.00032 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3128 on 269 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1594, Adjusted R-squared:  0.1438 
## F-statistic:  10.2 on 5 and 269 DF,  p-value: 5.794e-09</code></pre>
<pre class="r"><code>em_nodeath2.1 &lt;- lm(study1_dv_1 ~ factor(trgrp), data = subset(df2, coviddeath == 0), weights = teamweight)
summary(em_nodeath2.1)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(trgrp), data = subset(df2, 
##     coviddeath == 0), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.44154 -0.21521  0.06725  0.22456  1.15073 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.39511    0.02766  14.285  &lt; 2e-16 ***
## factor(trgrp)2  0.14974    0.03891   3.849 0.000129 ***
## factor(trgrp)3 -0.06202    0.04152  -1.494 0.135732    
## factor(trgrp)4  0.01794    0.04041   0.444 0.657235    
## factor(trgrp)5  0.16521    0.04126   4.004 6.88e-05 ***
## factor(trgrp)6  0.35330    0.04159   8.495  &lt; 2e-16 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3233 on 714 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.1465, Adjusted R-squared:  0.1405 
## F-statistic: 24.51 on 5 and 714 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code># table G.1
modelsummary(models = list(&#39;COVID Death&#39;  = em_death1.1,
                           &#39;No COVID Death&#39; = em_nodeath1.1,
                           &#39;COVID Death&#39;  = em_death2.1,
                           &#39;No COVID Death&#39; = em_nodeath2.1),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>## Sympathy

em_death1.2 &lt;- lm(study1_dv2_1 ~ factor(tr1), data = subset(df, coviddeath == 1))
summary(em_death1.2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr1), data = subset(df, coviddeath == 
##     1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.67167 -0.17151  0.05782  0.21691  0.41845 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.76396    0.02851  26.794  &lt; 2e-16 ***
## factor(tr1)2 -0.17116    0.04872  -3.513 0.000502 ***
## factor(tr1)3  0.03136    0.04973   0.631 0.528735    
## factor(tr1)4 -0.09255    0.04671  -1.981 0.048348 *  
## factor(tr1)5 -0.09229    0.05168  -1.786 0.075022 .  
## factor(tr1)6 -0.18241    0.04646  -3.926 0.000104 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2794 on 344 degrees of freedom
## Multiple R-squared:  0.07677,    Adjusted R-squared:  0.06335 
## F-statistic: 5.721 on 5 and 344 DF,  p-value: 4.366e-05</code></pre>
<pre class="r"><code>em_nodeath1.2 &lt;- lm(study1_dv2_1 ~ factor(tr1), data = subset(df, coviddeath == 0))
summary(em_nodeath1.2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr1), data = subset(df, coviddeath == 
##     0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.70883 -0.20883  0.03342  0.27342  0.54199 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.708833   0.017528  40.440  &lt; 2e-16 ***
## factor(tr1)2 -0.196820   0.030427  -6.469 1.54e-10 ***
## factor(tr1)3 -0.008692   0.030924  -0.281  0.77870    
## factor(tr1)4 -0.102789   0.031544  -3.259  0.00116 ** 
## factor(tr1)5 -0.212258   0.030635  -6.929 7.60e-12 ***
## factor(tr1)6 -0.250819   0.031384  -7.992 3.64e-15 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3036 on 1001 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.1016, Adjusted R-squared:  0.09716 
## F-statistic: 22.65 on 5 and 1001 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>em_death2.2 &lt;- lm(study1_dv2_1 ~ factor(trgrp), data = subset(df2, coviddeath == 1))
summary(em_death2.2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(trgrp), data = subset(df2, 
##     coviddeath == 1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.73489 -0.23489  0.03963  0.25378  0.61708 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.73489    0.04560  16.115  &lt; 2e-16 ***
## factor(trgrp)2 -0.30607    0.06951  -4.403 1.54e-05 ***
## factor(trgrp)3 -0.01297    0.06229  -0.208  0.83526    
## factor(trgrp)4 -0.17156    0.06348  -2.703  0.00732 ** 
## factor(trgrp)5 -0.30297    0.06380  -4.749 3.34e-06 ***
## factor(trgrp)6 -0.35197    0.06348  -5.545 7.05e-08 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3059 on 268 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1819, Adjusted R-squared:  0.1667 
## F-statistic: 11.92 on 5 and 268 DF,  p-value: 1.984e-10</code></pre>
<pre class="r"><code>em_nodeath2.2 &lt;- lm(study1_dv2_1 ~ factor(trgrp), data = subset(df2, coviddeath == 0))
summary(em_nodeath2.2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(trgrp), data = subset(df2, 
##     coviddeath == 0))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6928 -0.2028  0.0256  0.2504  0.5771 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)     0.65992    0.02750  23.997  &lt; 2e-16 ***
## factor(trgrp)2 -0.16030    0.03807  -4.210 2.88e-05 ***
## factor(trgrp)3  0.04293    0.03931   1.092   0.2752    
## factor(trgrp)4 -0.06908    0.03897  -1.773   0.0767 .  
## factor(trgrp)5 -0.17397    0.03931  -4.426 1.11e-05 ***
## factor(trgrp)6 -0.23701    0.03922  -6.043 2.44e-09 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3025 on 716 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.09659,    Adjusted R-squared:  0.09028 
## F-statistic: 15.31 on 5 and 716 DF,  p-value: 2.572e-14</code></pre>
<pre class="r"><code># table G.2
modelsummary(models = list(&#39;COVID Death&#39;  = em_death1.2,
                           &#39;No COVID Death&#39; = em_nodeath1.2,
                           &#39;COVID Death&#39;  = em_death2.2,
                           &#39;No COVID Death&#39; = em_nodeath2.2),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>## Testing by Vax Status

# df$vaxstatus2 &lt;- ifelse((df$vaxstatus == 1 | df$vaxstatus == 2), 1,
#                  ifelse(df$vaxstatus == 3, 0, 2))

vac1.1_vax &lt;- lm(study1_dv_1 ~ factor(tr1), data = subset(df, vaxstatus2 == 1))
summary(vac1.1_vax)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr1), data = subset(df, vaxstatus2 == 
##     1))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7414 -0.2918  0.0397  0.2623  0.6247 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.42197    0.01935  21.808  &lt; 2e-16 ***
## factor(tr1)2  0.31575    0.03348   9.432  &lt; 2e-16 ***
## factor(tr1)3 -0.04670    0.03325  -1.405    0.160    
## factor(tr1)4  0.04832    0.03429   1.409    0.159    
## factor(tr1)5  0.28520    0.03465   8.232 5.85e-16 ***
## factor(tr1)6  0.31940    0.03404   9.384  &lt; 2e-16 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3289 on 980 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.1815, Adjusted R-squared:  0.1773 
## F-statistic: 43.46 on 5 and 980 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>vac1.1_novax &lt;- lm(study1_dv_1 ~ factor(tr1), data = subset(df, vaxstatus2 == 0))
summary(vac1.1_novax)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ factor(tr1), data = subset(df, vaxstatus2 == 
##     0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.50771 -0.25160 -0.01771  0.24229  0.74840 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.25160    0.03300   7.623 2.90e-13 ***
## factor(tr1)2  0.14218    0.05801   2.451  0.01478 *  
## factor(tr1)3  0.06915    0.06041   1.145  0.25315    
## factor(tr1)4  0.06800    0.05638   1.206  0.22871    
## factor(tr1)5  0.25611    0.05677   4.512 9.07e-06 ***
## factor(tr1)6  0.16883    0.05716   2.953  0.00338 ** 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.32 on 317 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.07152,    Adjusted R-squared:  0.05688 
## F-statistic: 4.884 on 5 and 317 DF,  p-value: 0.0002552</code></pre>
<pre class="r"><code>vac1.2_vax &lt;- lm(study1_dv2_1 ~ factor(tr1), data = subset(df, vaxstatus2 == 1))
summary(vac1.2_vax)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr1), data = subset(df, vaxstatus2 == 
##     1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.74946 -0.22283  0.03361  0.25054  0.52717 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.71639    0.01758  40.758  &lt; 2e-16 ***
## factor(tr1)2 -0.20619    0.03041  -6.780 2.06e-11 ***
## factor(tr1)3  0.03306    0.03034   1.090   0.2761    
## factor(tr1)4 -0.07980    0.03122  -2.556   0.0107 *  
## factor(tr1)5 -0.19578    0.03155  -6.206 8.00e-10 ***
## factor(tr1)6 -0.24357    0.03099  -7.859 1.01e-14 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2998 on 982 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.113,  Adjusted R-squared:  0.1085 
## F-statistic: 25.02 on 5 and 982 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>vac1.2_novax &lt;- lm(study1_dv2_1 ~ factor(tr1), data = subset(df, vaxstatus2 == 0))
summary(vac1.2_novax)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ factor(tr1), data = subset(df, vaxstatus2 == 
##     0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.73660 -0.18660  0.00792  0.25840  0.44792 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.73660    0.03099  23.766  &lt; 2e-16 ***
## factor(tr1)2 -0.11215    0.05447  -2.059 0.040319 *  
## factor(tr1)3 -0.10485    0.05673  -1.848 0.065496 .  
## factor(tr1)4 -0.14088    0.05295  -2.661 0.008191 ** 
## factor(tr1)5 -0.18451    0.05331  -3.461 0.000611 ***
## factor(tr1)6 -0.16787    0.05368  -3.127 0.001929 ** 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3005 on 317 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.05256,    Adjusted R-squared:  0.03761 
## F-statistic: 3.517 on 5 and 317 DF,  p-value: 0.004146</code></pre>
<pre class="r"><code># table G.3
modelsummary(models = list(&#39;Vaccinated&#39;  = vac1.1_vax,
                           &#39;Not Vaccinated&#39; = vac1.1_novax,
                           &#39;Vaccinated&#39;  = vac1.2_vax,
                           &#39;Not Vaccinated&#39; = vac1.2_novax),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
</div>
<div id="dims-tukey-multiple-pairwise-comparisons" class="section level2">
<h2>DIMs (Tukey Multiple pairwise-comparisons)</h2>
<pre class="r"><code>aov_b1r &lt;- aov(study1_dv_1 ~ as.factor(tr1), data = subset(df, gop == 1))
dim_b1r &lt;- TukeyHSD(aov_b1r)
aov_b2r &lt;- aov(study1_dv_1 ~ as.factor(trgrp), data = subset(df2, gop == 1))
dim_b2r &lt;- TukeyHSD(aov_b2r)

aov_s1r &lt;- aov(study1_dv_2 ~ as.factor(tr1), data = subset(df, gop == 1))
dim_s1r &lt;- TukeyHSD(aov_s1r)
aov_s2r &lt;- aov(study1_dv_2 ~ as.factor(trgrp), data = subset(df2, gop == 1))
dim_s2r &lt;- TukeyHSD(aov_s2r)


aov_b1d &lt;- aov(study1_dv_1 ~ as.factor(tr1), data = subset(df, gop == 0))
dim_b1d &lt;- TukeyHSD(aov_b1d)
aov_b2d &lt;- aov(study1_dv_1 ~ as.factor(trgrp), data = subset(df2, gop == 0))
dim_b2d &lt;- TukeyHSD(aov_b2d)

aov_s1d &lt;- aov(study1_dv_2 ~ as.factor(tr1), data = subset(df, gop == 0))
dim_s1d &lt;- TukeyHSD(aov_s1d)
aov_s2d &lt;- aov(study1_dv_2 ~ as.factor(trgrp), data = subset(df2, gop == 0))
dim_s2d &lt;- TukeyHSD(aov_s2d)

dim_b1r &lt;- tidy(dim_b1r)
dim_b2r &lt;- tidy(dim_b2r)
dim_b1d &lt;- tidy(dim_b1d)
dim_b2d &lt;- tidy(dim_b2d)

dim_s1r &lt;- tidy(dim_s1r)
dim_s2r &lt;- tidy(dim_s2r)
dim_s1d &lt;- tidy(dim_s1d)
dim_s2d &lt;- tidy(dim_s2d)


library(broom) # creates tables E.1 through E.8
print.xtable(xtable(dim_b1r[c(2, 4, 7)]), include.rownames = F)</code></pre>
<pre><code>## % latex table generated in R 4.3.3 by xtable 1.8-4 package
## % Mon Aug  4 12:04:41 2025
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrr}
##   \hline
## contrast &amp; estimate &amp; adj.p.value \\ 
##   \hline
## 2-1 &amp; 0.24 &amp; 0.00 \\ 
##   3-1 &amp; 0.00 &amp; 1.00 \\ 
##   4-1 &amp; -0.01 &amp; 1.00 \\ 
##   5-1 &amp; 0.28 &amp; 0.00 \\ 
##   6-1 &amp; 0.28 &amp; 0.00 \\ 
##   3-2 &amp; -0.24 &amp; 0.00 \\ 
##   4-2 &amp; -0.25 &amp; 0.00 \\ 
##   5-2 &amp; 0.04 &amp; 0.98 \\ 
##   6-2 &amp; 0.04 &amp; 0.99 \\ 
##   4-3 &amp; -0.01 &amp; 1.00 \\ 
##   5-3 &amp; 0.28 &amp; 0.00 \\ 
##   6-3 &amp; 0.28 &amp; 0.00 \\ 
##   5-4 &amp; 0.29 &amp; 0.00 \\ 
##   6-4 &amp; 0.29 &amp; 0.00 \\ 
##   6-5 &amp; -0.00 &amp; 1.00 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
<pre class="r"><code>print.xtable(xtable(dim_b1d[c(2, 4, 7)]), include.rownames = F)</code></pre>
<pre><code>## % latex table generated in R 4.3.3 by xtable 1.8-4 package
## % Mon Aug  4 12:04:41 2025
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrr}
##   \hline
## contrast &amp; estimate &amp; adj.p.value \\ 
##   \hline
## 2-1 &amp; 0.28 &amp; 0.00 \\ 
##   3-1 &amp; -0.04 &amp; 0.92 \\ 
##   4-1 &amp; 0.08 &amp; 0.34 \\ 
##   5-1 &amp; 0.28 &amp; 0.00 \\ 
##   6-1 &amp; 0.29 &amp; 0.00 \\ 
##   3-2 &amp; -0.32 &amp; 0.00 \\ 
##   4-2 &amp; -0.20 &amp; 0.00 \\ 
##   5-2 &amp; -0.01 &amp; 1.00 \\ 
##   6-2 &amp; 0.01 &amp; 1.00 \\ 
##   4-3 &amp; 0.12 &amp; 0.10 \\ 
##   5-3 &amp; 0.32 &amp; 0.00 \\ 
##   6-3 &amp; 0.33 &amp; 0.00 \\ 
##   5-4 &amp; 0.20 &amp; 0.00 \\ 
##   6-4 &amp; 0.21 &amp; 0.00 \\ 
##   6-5 &amp; 0.01 &amp; 1.00 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
<pre class="r"><code>print.xtable(xtable(dim_b2r[c(2, 4, 7)]), include.rownames = F)</code></pre>
<pre><code>## % latex table generated in R 4.3.3 by xtable 1.8-4 package
## % Mon Aug  4 12:04:41 2025
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrr}
##   \hline
## contrast &amp; estimate &amp; adj.p.value \\ 
##   \hline
## 2-1 &amp; 0.16 &amp; 0.12 \\ 
##   3-1 &amp; -0.06 &amp; 0.94 \\ 
##   4-1 &amp; -0.03 &amp; 1.00 \\ 
##   5-1 &amp; 0.18 &amp; 0.07 \\ 
##   6-1 &amp; 0.17 &amp; 0.09 \\ 
##   3-2 &amp; -0.22 &amp; 0.01 \\ 
##   4-2 &amp; -0.19 &amp; 0.02 \\ 
##   5-2 &amp; 0.02 &amp; 1.00 \\ 
##   6-2 &amp; 0.01 &amp; 1.00 \\ 
##   4-3 &amp; 0.03 &amp; 1.00 \\ 
##   5-3 &amp; 0.24 &amp; 0.01 \\ 
##   6-3 &amp; 0.23 &amp; 0.01 \\ 
##   5-4 &amp; 0.21 &amp; 0.01 \\ 
##   6-4 &amp; 0.20 &amp; 0.01 \\ 
##   6-5 &amp; -0.01 &amp; 1.00 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
<pre class="r"><code>print.xtable(xtable(dim_b2d[c(2, 4, 7)]), include.rownames = F)</code></pre>
<pre><code>## % latex table generated in R 4.3.3 by xtable 1.8-4 package
## % Mon Aug  4 12:04:41 2025
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrr}
##   \hline
## contrast &amp; estimate &amp; adj.p.value \\ 
##   \hline
## 2-1 &amp; 0.33 &amp; 0.00 \\ 
##   3-1 &amp; -0.08 &amp; 0.47 \\ 
##   4-1 &amp; 0.10 &amp; 0.25 \\ 
##   5-1 &amp; 0.29 &amp; 0.00 \\ 
##   6-1 &amp; 0.39 &amp; 0.00 \\ 
##   3-2 &amp; -0.41 &amp; 0.00 \\ 
##   4-2 &amp; -0.23 &amp; 0.00 \\ 
##   5-2 &amp; -0.04 &amp; 0.97 \\ 
##   6-2 &amp; 0.06 &amp; 0.73 \\ 
##   4-3 &amp; 0.18 &amp; 0.00 \\ 
##   5-3 &amp; 0.37 &amp; 0.00 \\ 
##   6-3 &amp; 0.47 &amp; 0.00 \\ 
##   5-4 &amp; 0.19 &amp; 0.00 \\ 
##   6-4 &amp; 0.29 &amp; 0.00 \\ 
##   6-5 &amp; 0.10 &amp; 0.23 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
<pre class="r"><code>print.xtable(xtable(dim_s1r[c(2, 4, 7)]), include.rownames = F)</code></pre>
<pre><code>## % latex table generated in R 4.3.3 by xtable 1.8-4 package
## % Mon Aug  4 12:04:41 2025
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrr}
##   \hline
## contrast &amp; estimate &amp; adj.p.value \\ 
##   \hline
## 2-1 &amp; -0.10 &amp; 0.31 \\ 
##   3-1 &amp; -0.02 &amp; 1.00 \\ 
##   4-1 &amp; -0.01 &amp; 1.00 \\ 
##   5-1 &amp; 0.05 &amp; 0.89 \\ 
##   6-1 &amp; 0.01 &amp; 1.00 \\ 
##   3-2 &amp; 0.08 &amp; 0.72 \\ 
##   4-2 &amp; 0.09 &amp; 0.62 \\ 
##   5-2 &amp; 0.15 &amp; 0.06 \\ 
##   6-2 &amp; 0.11 &amp; 0.42 \\ 
##   4-3 &amp; 0.01 &amp; 1.00 \\ 
##   5-3 &amp; 0.07 &amp; 0.80 \\ 
##   6-3 &amp; 0.03 &amp; 1.00 \\ 
##   5-4 &amp; 0.06 &amp; 0.86 \\ 
##   6-4 &amp; 0.02 &amp; 1.00 \\ 
##   6-5 &amp; -0.04 &amp; 0.97 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
<pre class="r"><code>print.xtable(xtable(dim_s1d[c(2, 4, 7)]), include.rownames = F)</code></pre>
<pre><code>## % latex table generated in R 4.3.3 by xtable 1.8-4 package
## % Mon Aug  4 12:04:41 2025
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrr}
##   \hline
## contrast &amp; estimate &amp; adj.p.value \\ 
##   \hline
## 2-1 &amp; 0.03 &amp; 0.98 \\ 
##   3-1 &amp; 0.01 &amp; 1.00 \\ 
##   4-1 &amp; -0.02 &amp; 1.00 \\ 
##   5-1 &amp; -0.05 &amp; 0.91 \\ 
##   6-1 &amp; 0.01 &amp; 1.00 \\ 
##   3-2 &amp; -0.03 &amp; 1.00 \\ 
##   4-2 &amp; -0.05 &amp; 0.89 \\ 
##   5-2 &amp; -0.08 &amp; 0.66 \\ 
##   6-2 &amp; -0.02 &amp; 1.00 \\ 
##   4-3 &amp; -0.03 &amp; 0.99 \\ 
##   5-3 &amp; -0.05 &amp; 0.91 \\ 
##   6-3 &amp; 0.00 &amp; 1.00 \\ 
##   5-4 &amp; -0.02 &amp; 1.00 \\ 
##   6-4 &amp; 0.03 &amp; 0.99 \\ 
##   6-5 &amp; 0.06 &amp; 0.88 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
<pre class="r"><code>print.xtable(xtable(dim_s2r[c(2, 4, 7)]), include.rownames = F)</code></pre>
<pre><code>## % latex table generated in R 4.3.3 by xtable 1.8-4 package
## % Mon Aug  4 12:04:41 2025
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrr}
##   \hline
## contrast &amp; estimate &amp; adj.p.value \\ 
##   \hline
## 2-1 &amp; 0.01 &amp; 1.00 \\ 
##   3-1 &amp; 0.02 &amp; 1.00 \\ 
##   4-1 &amp; 0.06 &amp; 0.92 \\ 
##   5-1 &amp; -0.06 &amp; 0.90 \\ 
##   6-1 &amp; 0.08 &amp; 0.74 \\ 
##   3-2 &amp; 0.01 &amp; 1.00 \\ 
##   4-2 &amp; 0.05 &amp; 0.96 \\ 
##   5-2 &amp; -0.07 &amp; 0.83 \\ 
##   6-2 &amp; 0.07 &amp; 0.83 \\ 
##   4-3 &amp; 0.04 &amp; 0.99 \\ 
##   5-3 &amp; -0.08 &amp; 0.77 \\ 
##   6-3 &amp; 0.06 &amp; 0.91 \\ 
##   5-4 &amp; -0.12 &amp; 0.29 \\ 
##   6-4 &amp; 0.02 &amp; 1.00 \\ 
##   6-5 &amp; 0.14 &amp; 0.14 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
<pre class="r"><code>print.xtable(xtable(dim_s2d[c(2, 4, 7)]), include.rownames = F)</code></pre>
<pre><code>## % latex table generated in R 4.3.3 by xtable 1.8-4 package
## % Mon Aug  4 12:04:41 2025
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrr}
##   \hline
## contrast &amp; estimate &amp; adj.p.value \\ 
##   \hline
## 2-1 &amp; -0.08 &amp; 0.50 \\ 
##   3-1 &amp; 0.01 &amp; 1.00 \\ 
##   4-1 &amp; -0.02 &amp; 1.00 \\ 
##   5-1 &amp; -0.07 &amp; 0.60 \\ 
##   6-1 &amp; -0.13 &amp; 0.05 \\ 
##   3-2 &amp; 0.09 &amp; 0.32 \\ 
##   4-2 &amp; 0.06 &amp; 0.83 \\ 
##   5-2 &amp; 0.01 &amp; 1.00 \\ 
##   6-2 &amp; -0.05 &amp; 0.88 \\ 
##   4-3 &amp; -0.03 &amp; 0.98 \\ 
##   5-3 &amp; -0.08 &amp; 0.40 \\ 
##   6-3 &amp; -0.14 &amp; 0.02 \\ 
##   5-4 &amp; -0.05 &amp; 0.89 \\ 
##   6-4 &amp; -0.11 &amp; 0.19 \\ 
##   6-5 &amp; -0.06 &amp; 0.80 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
</div>
<div id="summary-statistics-and-diagnostics" class="section level1">
<h1>Summary Statistics and Diagnostics</h1>
<div id="survey-stats" class="section level2">
<h2>Survey Stats</h2>
<pre class="r"><code>round(colMeans(df, na.rm = T), 2) # to create table B.3--vaxstatus2 is the proper vaccine status variable to use</code></pre>
<pre><code>##         CaseID            age         female          black       hispanic 
##         722.50          46.58           0.49           0.12           0.06 
##      education            tr1    study1_dv_1    study1_dv_2   study1_dv2_1 
##           3.27           3.12           0.50           0.38           0.62 
##    study1_dv_3            pid            gop     coviddeath      vaxstatus 
##           0.39           3.67           0.43           0.26           1.79 
## covidconcern_1 covidvaccine_1         tr_dem         tr_rep         tr_pid 
##           0.64           0.60           0.28           0.28           0.85 
##         tr_vax     vaxstatus2 
##           0.43           0.75</code></pre>
<pre class="r"><code>format(round(colMeans(df2, na.rm = T), 3), scientific = F) # to create table B.3</code></pre>
<pre><code>##           caseid              age           female            black 
## &quot;1642908179.328&quot; &quot;        49.865&quot; &quot;         0.573&quot; &quot;         0.127&quot; 
##         hispanic             educ            trgrp      study1_dv_1 
## &quot;         0.048&quot; &quot;         3.663&quot; &quot;         3.491&quot; &quot;         0.534&quot; 
##      study1_dv_2     study1_dv2_1      study1_dv_3              pid 
## &quot;         0.310&quot; &quot;         0.557&quot; &quot;         0.376&quot; &quot;         3.608&quot; 
##              gop       teamweight       coviddeath        vaxstatus 
## &quot;         0.416&quot; &quot;         1.000&quot; &quot;         0.276&quot; &quot;         0.756&quot; 
##     covidconcern          mandate           tr_dem           tr_rep 
## &quot;         0.587&quot; &quot;         0.591&quot; &quot;         0.332&quot; &quot;         0.334&quot; 
##           tr_pid           tr_vax 
## &quot;         1.000&quot; &quot;         0.496&quot;</code></pre>
<pre class="r"><code># Descriptives of DVs

hist.df &lt;- as.data.frame(rbind(df[, c(&#39;study1_dv_1&#39;, &#39;study1_dv2_1&#39;)], df2[, c(&#39;study1_dv_1&#39;, &#39;study1_dv2_1&#39;)]))
hist.df$study &lt;- c(rep(&#39;Study I&#39;, 1444), rep(&#39;Study II&#39;, 1000))
names(hist.df) &lt;- c(&#39;Blame&#39;, &#39;Sympathy&#39;, &#39;Study&#39;)


dv_hist1 &lt;- ggplot(hist.df, aes(x = Blame)) + # creates figure B.6
  geom_histogram(aes(y = ..density..), binwidth = 0.01) +
  scale_y_continuous(labels = scales::percent_format()) +
  facet_wrap(~Study) +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.position=&quot;bottom&quot;) +
  theme(text=element_text(family=&quot;serif&quot;))

dv_hist2 &lt;- ggplot(hist.df, aes(x = Sympathy)) + # creates figure B.7
  geom_histogram(aes(y = ..density..), binwidth = 0.01) +
  scale_y_continuous(labels = scales::percent_format()) +
  facet_wrap(~Study) +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.position=&quot;bottom&quot;) +
  theme(text=element_text(family=&quot;serif&quot;))

# ggsave(&#39;dv_hist1.png&#39;, dv_hist1, &#39;png&#39;, &#39;~/Dropbox/Wayde/Research/Apps/Overleaf/CovidPolar_JEPS&#39;,
#     width = 190, height = 127, units = &#39;mm&#39;)
# ggsave(&#39;dv_hist2.png&#39;, dv_hist2, &#39;png&#39;, &#39;~/Dropbox/Wayde/Research/Apps/Overleaf/CovidPolar_JEPS&#39;,
#     width = 190, height = 127, units = &#39;mm&#39;)</code></pre>
</div>
<div id="means-of-dvs" class="section level2">
<h2>Means of DVs</h2>
<pre class="r"><code>m1_blame &lt;- aggregate(study1_dv_1 ~ tr1, data = df, FUN = mean)
m1_symp &lt;- aggregate(study1_dv_2 ~ tr1, data = df, FUN = mean)
s1_blame &lt;- aggregate(study1_dv_1 ~ tr1, data = df, FUN = sd)
s1_symp &lt;- aggregate(study1_dv_2 ~ tr1, data = df, FUN = sd)

m2_blame &lt;- aggregate(study1_dv_1 ~ trgrp, data = df2, FUN = mean)
m2_symp &lt;- aggregate(study1_dv_2 ~ trgrp, data = df2, FUN = mean)
s2_blame &lt;- aggregate(study1_dv_1 ~ trgrp, data = df2, FUN = sd)
s2_symp &lt;- aggregate(study1_dv_2 ~ trgrp, data = df2, FUN = sd)

names(m1_blame) &lt;- c(&#39;trgrp&#39;, &#39;mean&#39;)
names(m1_symp) &lt;- c(&#39;trgrp&#39;, &#39;mean&#39;)
names(m2_blame) &lt;- c(&#39;trgrp&#39;, &#39;mean&#39;)
names(m2_symp) &lt;- c(&#39;trgrp&#39;, &#39;mean&#39;)
names(s1_blame) &lt;- c(&#39;trgrp&#39;, &#39;sd&#39;)
names(s1_symp) &lt;- c(&#39;trgrp&#39;, &#39;sd&#39;)
names(s2_blame) &lt;- c(&#39;trgrp&#39;, &#39;sd&#39;)
names(s2_symp) &lt;- c(&#39;trgrp&#39;, &#39;sd&#39;)

names_vec &lt;- rbind(m1_blame[1], m2_blame[1],
                m1_symp[1], m2_symp[1])
means_vec &lt;- rbind(m1_blame[2], m2_blame[2],
                m1_symp[2], m2_symp[2])
sd_vec &lt;- rbind(s1_blame[2], s2_blame[2],
                s1_symp[2], s2_symp[2])
sum_df &lt;- cbind(names_vec, means_vec, sd_vec)

sum_df &lt;- sum_df %&gt;%
  mutate(trgrp = recode(trgrp, &#39;1&#39; = &#39;Control&#39;, &#39;2&#39; = &#39;Anti-Vax&#39;, &#39;3&#39; = &#39;Democrat&#39;, &#39;4&#39; = &#39;Republican&#39;,
                        &#39;5&#39; = &#39;Anti-Vax Democrat&#39;, &#39;6&#39; = &#39;Anti-Vax Republican&#39;))
sum_df$dv &lt;- c(rep(&#39;Blame DV&#39;, 12), rep(&#39;Sympathy DV&#39;, 12))
sum_df$study &lt;- c(rep(&#39;Study I&#39;, 6), rep(&#39;Study II&#39;, 6),
                  rep(&#39;Study I&#39;, 6), rep(&#39;Study II&#39;, 6))

library(xtable) # tables B.1 and B.2
print.xtable(xtable(sum_df[sum_df$dv == &#39;Blame DV&#39;, c(1:3, 5)], type = &#39;latex&#39;), include.rownames = F)</code></pre>
<pre><code>## % latex table generated in R 4.3.3 by xtable 1.8-4 package
## % Mon Aug  4 12:04:41 2025
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrl}
##   \hline
## trgrp &amp; mean &amp; sd &amp; study \\ 
##   \hline
## Control &amp; 0.38 &amp; 0.34 &amp; Study I \\ 
##   Anti-Vax &amp; 0.65 &amp; 0.35 &amp; Study I \\ 
##   Democrat &amp; 0.36 &amp; 0.35 &amp; Study I \\ 
##   Republican &amp; 0.42 &amp; 0.36 &amp; Study I \\ 
##   Anti-Vax Democrat &amp; 0.65 &amp; 0.32 &amp; Study I \\ 
##   Anti-Vax Republican &amp; 0.66 &amp; 0.33 &amp; Study I \\ 
##   Control &amp; 0.40 &amp; 0.30 &amp; Study II \\ 
##   Anti-Vax &amp; 0.63 &amp; 0.34 &amp; Study II \\ 
##   Democrat &amp; 0.35 &amp; 0.29 &amp; Study II \\ 
##   Republican &amp; 0.46 &amp; 0.35 &amp; Study II \\ 
##   Anti-Vax Democrat &amp; 0.66 &amp; 0.34 &amp; Study II \\ 
##   Anti-Vax Republican &amp; 0.72 &amp; 0.31 &amp; Study II \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
<pre class="r"><code>print.xtable(xtable(sum_df[sum_df$dv == &#39;Sympathy DV&#39;, c(1:3, 5)], type = &#39;latex&#39;), include.rownames = F)</code></pre>
<pre><code>## % latex table generated in R 4.3.3 by xtable 1.8-4 package
## % Mon Aug  4 12:04:41 2025
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrl}
##   \hline
## trgrp &amp; mean &amp; sd &amp; study \\ 
##   \hline
## Control &amp; 0.39 &amp; 0.34 &amp; Study I \\ 
##   Anti-Vax &amp; 0.35 &amp; 0.34 &amp; Study I \\ 
##   Democrat &amp; 0.38 &amp; 0.35 &amp; Study I \\ 
##   Republican &amp; 0.37 &amp; 0.34 &amp; Study I \\ 
##   Anti-Vax Democrat &amp; 0.38 &amp; 0.35 &amp; Study I \\ 
##   Anti-Vax Republican &amp; 0.38 &amp; 0.35 &amp; Study I \\ 
##   Control &amp; 0.35 &amp; 0.28 &amp; Study II \\ 
##   Anti-Vax &amp; 0.32 &amp; 0.32 &amp; Study II \\ 
##   Democrat &amp; 0.33 &amp; 0.27 &amp; Study II \\ 
##   Republican &amp; 0.31 &amp; 0.30 &amp; Study II \\ 
##   Anti-Vax Democrat &amp; 0.28 &amp; 0.30 &amp; Study II \\ 
##   Anti-Vax Republican &amp; 0.28 &amp; 0.29 &amp; Study II \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
<div id="balance-analysis" class="section level2">
<h2>Balance Analysis</h2>
<pre class="r"><code>df_complete &lt;- na.omit(df)
df2_complete &lt;- na.omit(df2)

model1_age &lt;- aov(age ~ as.factor(tr1), data = df_complete)
model1_edu &lt;- aov(education ~ as.factor(tr1), data = df_complete)
model1_sex &lt;- aov(female ~ as.factor(tr1), data = df_complete)
model1_blk &lt;- aov(black ~ as.factor(tr1), data = df_complete)
model1_hsp &lt;- aov(hispanic ~ as.factor(tr1), data = df_complete)
model1_pid &lt;- aov(pid ~ as.factor(tr1), data = df_complete)

model2_age &lt;- aov(age ~ as.factor(trgrp), data = df2_complete)
model2_edu &lt;- aov(educ ~ as.factor(trgrp), data = df2_complete)
model2_sex &lt;- aov(female ~ as.factor(trgrp), data = df2_complete)
model2_blk &lt;- aov(black ~ as.factor(trgrp), data = df2_complete)
model2_hsp &lt;- aov(hispanic ~ as.factor(trgrp), data = df2_complete)
model2_pid &lt;- aov(pid ~ as.factor(trgrp), data = df2_complete)

gg_tukey&lt;-function(Tukey){
  A&lt;-require(&quot;tidyverse&quot;)
  if(A==TRUE){
    library(tidyverse)
  } else {
    install.packages(&quot;tidyverse&quot;)
    library(tidyverse)
  }
  B&lt;-as.data.frame(Tukey[1])
  colnames(B)[2:3]&lt;-c(&quot;min&quot;,
                      &quot;max&quot;)
  C&lt;-data.frame(id=row.names(B),
                min=B$min,
                max=B$max)
  D&lt;-C%&gt;%
    ggplot(aes(id))+
    geom_errorbar(aes(ymin=min,
                     ymax=max),
                 width = 0.2)+
    geom_hline(yintercept=0,
               color=&quot;red&quot;)+
    labs(x=NULL)+
    coord_flip()+
    theme(text=element_text(family=&quot;serif&quot;),
            title=element_text(color=&quot;black&quot;,size=15),
            axis.text = element_text(color=&quot;black&quot;,size=10),
            axis.title = element_text(color=&quot;black&quot;,size=10),
            panel.grid=element_line(color=&quot;grey75&quot;),
            axis.line=element_blank(),
            plot.background=element_rect(fill=&quot;white&quot;,color=&quot;white&quot;),
            panel.background=element_rect(fill=&quot;white&quot;),
            panel.border = element_rect(colour = &quot;black&quot;, fill = NA,size=0.59),
            legend.key= element_rect(color=&quot;white&quot;,fill=&quot;white&quot;)
          )
  return(D)
}

tk1_1 &lt;- gg_tukey(TukeyHSD(model1_age))</code></pre>
<pre><code>## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.</code></pre>
<pre class="r"><code>tk1_2 &lt;- gg_tukey(TukeyHSD(model1_edu))
tk1_3 &lt;- gg_tukey(TukeyHSD(model1_sex))
tk1_4 &lt;- gg_tukey(TukeyHSD(model1_blk))
tk1_5 &lt;- gg_tukey(TukeyHSD(model1_hsp))
tk1_6 &lt;- gg_tukey(TukeyHSD(model1_pid))

tk2_1 &lt;- gg_tukey(TukeyHSD(model2_age))
tk2_2 &lt;- gg_tukey(TukeyHSD(model2_edu))
tk2_3 &lt;- gg_tukey(TukeyHSD(model2_sex))
tk2_4 &lt;- gg_tukey(TukeyHSD(model2_blk))
tk2_5 &lt;- gg_tukey(TukeyHSD(model2_hsp))
tk2_6 &lt;- gg_tukey(TukeyHSD(model2_pid))

# tk1_1 &lt;- plot(TukeyHSD(model1_age, conf.level = 0.95), las = 2)
# tk1_2 &lt;- plot(TukeyHSD(model1_edu, conf.level = 0.95), las = 2)
# tk1_3 &lt;- plot(TukeyHSD(model1_sex, conf.level = 0.95), las = 2)
# tk1_4 &lt;- plot(TukeyHSD(model1_blk, conf.level = 0.95), las = 2)
# tk1_5 &lt;- plot(TukeyHSD(model1_hsp, conf.level = 0.95), las = 2)
# tk1_6 &lt;- plot(TukeyHSD(model1_pid, conf.level = 0.95), las = 2)
# 
# tk2_1 &lt;- plot(TukeyHSD(model2_age, conf.level = 0.95), las = 2)
# tk2_2 &lt;- plot(TukeyHSD(model2_edu, conf.level = 0.95), las = 2)
# tk2_3 &lt;- plot(TukeyHSD(model2_sex, conf.level = 0.95), las = 2)
# tk2_4 &lt;- plot(TukeyHSD(model2_blk, conf.level = 0.95), las = 2)
# tk2_5 &lt;- plot(TukeyHSD(model2_hsp, conf.level = 0.95), las = 2)
# tk2_6 &lt;- plot(TukeyHSD(model2_pid, conf.level = 0.95), las = 2)

tk1 &lt;- ggarrange(tk1_1, tk1_2, tk1_3, tk1_4, tk1_5, tk1_6,# creates figure B.8
          labels = c(&#39;Age&#39;, &#39;Education&#39;, &#39;Sex&#39;, &#39;Black&#39;, &#39;Hispanic&#39;, &#39;Party ID&#39;),
          ncol = 2, nrow = 3)

tk2 &lt;- ggarrange(tk2_1, tk2_2, tk2_3, tk2_4, tk2_5, tk2_6,# creates figure B.9
          labels = c(&#39;Age&#39;, &#39;Education&#39;, &#39;Sex&#39;, &#39;Black&#39;, &#39;Hispanic&#39;, &#39;Party ID&#39;),
          ncol = 2, nrow = 3)</code></pre>
</div>
</div>
<div id="additionalsecondary-figures-and-analysis" class="section level1">
<h1>Additional/Secondary Figures and Analysis</h1>
<div id="figures" class="section level2">
<h2>Figures</h2>
<pre class="r"><code># blaming victim

coef1 &lt;- c(m1_dem_1$coefficients[2:6], m1_gop_1$coefficients[2:6],
           m2_dem_1$coefficients[2:6], m2_gop_1$coefficients[2:6])
se1 &lt;- sqrt(diag(vcov(m1_dem_1))); se2 &lt;- sqrt(diag(vcov(m1_gop_1))); se3 &lt;- sqrt(diag(vcov(m2_dem_1))); se4 &lt;- sqrt(diag(vcov(m2_gop_1)))

hi1 &lt;- c(coef1[1]+(1.96*se1[2]), coef1[2]+(1.96*se1[3]),
            coef1[3]+(1.96*se1[4]), coef1[4]+(1.96*se1[5]),
            coef1[5]+(1.96*se1[6]),
         coef1[6]+(1.96*se2[2]), coef1[7]+(1.96*se2[3]),
            coef1[8]+(1.96*se2[4]), coef1[9]+(1.96*se2[5]),
            coef1[10]+(1.96*se2[6]),
         coef1[11]+(1.96*se3[2]), coef1[12]+(1.96*se3[3]),
            coef1[13]+(1.96*se3[4]), coef1[14]+(1.96*se3[5]),
            coef1[15]+(1.96*se3[6]),
         coef1[16]+(1.96*se4[2]), coef1[17]+(1.96*se4[3]),
            coef1[18]+(1.96*se4[4]), coef1[19]+(1.96*se4[5]),
            coef1[20]+(1.96*se4[6]))

lo1 &lt;- c(coef1[1]-(1.96*se1[2]), coef1[2]-(1.96*se1[3]),
            coef1[3]-(1.96*se1[4]), coef1[4]-(1.96*se1[5]),
            coef1[5]-(1.96*se1[6]),
         coef1[6]-(1.96*se2[2]), coef1[7]-(1.96*se2[3]),
            coef1[8]-(1.96*se2[4]), coef1[9]-(1.96*se2[5]),
            coef1[10]-(1.96*se2[6]),
         coef1[11]-(1.96*se3[2]), coef1[12]-(1.96*se3[3]),
            coef1[13]-(1.96*se3[4]), coef1[14]-(1.96*se3[5]),
            coef1[15]-(1.96*se3[6]),
         coef1[16]-(1.96*se4[2]), coef1[17]-(1.96*se4[3]),
            coef1[18]-(1.96*se4[4]), coef1[19]-(1.96*se4[5]),
            coef1[20]-(1.96*se4[6]))

study &lt;- c(rep(&#39;Study I&#39;, 10), rep(&#39;Study II&#39;, 10))
party &lt;- rep(c(&#39;Democratic&#39;, &#39;Democratic&#39;, &#39;Democratic&#39;, &#39;Democratic&#39;, &#39;Democratic&#39;,
               &#39;Republican&#39;, &#39;Republican&#39;, &#39;Republican&#39;, &#39;Republican&#39;,
               &#39;Republican&#39;), 2)
tr &lt;- rep(c(&#39;eAnti-Vax&#39;, &#39;dDemocrat&#39;, &#39;cRepublican&#39;,
            &#39;bAnti-Vax Democrat&#39;, &#39;aAnti-Vax Republican&#39;), 4)

df_1 &lt;- as.data.frame(cbind(coef1, hi1, lo1, party, tr, study))
df_1[, 1:3] &lt;- lapply(df_1[, 1:3], as.numeric)
df_1[, 4:6] &lt;- lapply(df_1[, 4:6], as.factor)
df_1$stupid &lt;- str_c(df_1$party, &#39;, &#39;, df_1$study)


plot1 &lt;- ggplot(df_1, aes(shape = party)) +
  theme_bw() +
  geom_vline(xintercept = 0, linetype = &#39;dashed&#39;) +
  geom_pointrange(aes(x = coef1, y = tr, xmin = lo1,
                         xmax = hi1), size = 1,
                     lwd = 1, position = position_dodge(width = 0.5), fill = &#39;WHITE&#39;) +
  scale_shape_manual(name = &#39;Partisanship&#39;,
                    labels = c(&#39;Democrats&#39;, &#39;Republicans&#39;),
                    values = c(15, 1)) +
  scale_y_discrete(name = &#39;Treatment Condition&#39;,
                   labels = c(&#39;Anti-Vax Republican&#39;, &#39;Anti-Vax Democrat&#39;,
                              &#39;Republican&#39;, &#39;Democrat&#39;, &#39;Anti-Vax&#39;)) +
  scale_x_continuous(&quot;Estimated Effect on Blame Attributed to Victim&quot;) +
  facet_wrap(~study) +
  labs(title = &#39;Partisans Blame Deceased Anti-Vaxxers and Outpartisans for Own Death&#39;,
       caption = &#39;\nNote: Estimated effects were calculated from two separate models (based on partisan identity). \nError bars represent 95/% confidence intervals. Full results table in Appendix E.&#39;) +
  theme(axis.text=element_text(size = 10)) + 
  theme(plot.title = element_text(hjust = 0.5)) + 
  theme(plot.caption = element_text(hjust = 0)) +
  theme(text=element_text(size = 10, family=&quot;serif&quot;))

plot1_sl &lt;- ggplot(df_1, aes(shape = party)) +
  theme_bw() +
  geom_vline(xintercept = 0, linetype = &#39;dashed&#39;) +
  geom_pointrange(aes(x = coef1, y = tr, xmin = lo1,
                         xmax = hi1), size = 1,
                     lwd = 1, position = position_dodge(width = 0.5), fill = &#39;WHITE&#39;) +
  scale_shape_manual(name = &#39;Partisanship&#39;,
                    labels = c(&#39;Democrats&#39;, &#39;Republicans&#39;),
                    values = c(15, 1)) +
  scale_y_discrete(name = &#39;Treatment Condition&#39;,
                   labels = c(&#39;Anti-Vax Republican&#39;, &#39;Anti-Vax Democrat&#39;,
                              &#39;Republican&#39;, &#39;Democrat&#39;, &#39;Anti-Vax&#39;)) +
  scale_x_continuous(&quot;Estimated Effect on Blame Attributed to Victim&quot;) +
  facet_wrap(~study) +
  labs(title = &#39;Partisans Blame Deceased Anti-Vaxxers and Outpartisans for Own Death&#39;) +
  theme(axis.text=element_text(size = 10)) + 
  theme(plot.title = element_text(hjust = 0.5)) + 
  theme(plot.caption = element_text(hjust = 0)) +
  theme(text=element_text(size = 10, family=&quot;serif&quot;))

# sympathy

coef2 &lt;- c(m1_dem_4$coefficients[2:6], m1_gop_4$coefficients[2:6],
           m2_dem_4$coefficients[2:6], m2_gop_4$coefficients[2:6])
se1 &lt;- sqrt(diag(vcov(m1_dem_4))); se2 &lt;- sqrt(diag(vcov(m1_gop_4))); se3 &lt;- sqrt(diag(vcov(m2_dem_4))); se4 &lt;- sqrt(diag(vcov(m2_gop_4)))

hi2 &lt;- c(coef2[1]+(1.96*se1[2]), coef2[2]+(1.96*se1[3]),
            coef2[3]+(1.96*se1[4]), coef2[4]+(1.96*se1[5]),
            coef2[5]+(1.96*se1[6]),
         coef2[6]+(1.96*se2[2]), coef2[7]+(1.96*se2[3]),
            coef2[8]+(1.96*se2[4]), coef2[9]+(1.96*se2[5]),
            coef2[10]+(1.96*se2[6]),
         coef2[11]+(1.96*se3[2]), coef2[12]+(1.96*se3[3]),
            coef2[13]+(1.96*se3[4]), coef2[14]+(1.96*se3[5]),
            coef2[15]+(1.96*se3[6]),
         coef2[16]+(1.96*se4[2]), coef2[17]+(1.96*se4[3]),
            coef2[18]+(1.96*se4[4]), coef2[19]+(1.96*se4[5]),
            coef2[20]+(1.96*se4[6]))

lo2 &lt;- c(coef2[1]-(1.96*se1[2]), coef2[2]-(1.96*se1[3]),
            coef2[3]-(1.96*se1[4]), coef2[4]-(1.96*se1[5]),
            coef2[5]-(1.96*se1[6]),
         coef2[6]-(1.96*se2[2]), coef2[7]-(1.96*se2[3]),
            coef2[8]-(1.96*se2[4]), coef2[9]-(1.96*se2[5]),
            coef2[10]-(1.96*se2[6]),
         coef2[11]-(1.96*se3[2]), coef2[12]-(1.96*se3[3]),
            coef2[13]-(1.96*se3[4]), coef2[14]-(1.96*se3[5]),
            coef2[15]-(1.96*se3[6]),
         coef2[16]-(1.96*se4[2]), coef2[17]-(1.96*se4[3]),
            coef2[18]-(1.96*se4[4]), coef2[19]-(1.96*se4[5]),
            coef2[20]-(1.96*se4[6]))

df_2 &lt;- as.data.frame(cbind(coef2, hi2, lo2, party, tr, study))
df_2[, 1:3] &lt;- lapply(df_2[, 1:3], as.numeric)
df_2[, 4:6] &lt;- lapply(df_2[, 4:6], as.factor)
df_2$stupid &lt;- str_c(df_2$party, &#39;, &#39;, df_2$study)


plot2 &lt;- ggplot(df_2, aes(shape = party)) +
  theme_bw() +
  geom_vline(xintercept = 0, linetype = &#39;dashed&#39;) +
  geom_pointrange(aes(x = coef2, y = tr, xmin = lo2,
                         xmax = hi2), size = 1,
                     lwd = 1, position = position_dodge(width = 0.5), fill = &#39;WHITE&#39;) +
  scale_shape_manual(name = &#39;Partisanship&#39;,
                    labels = c(&#39;Democrats&#39;, &#39;Republicans&#39;),
                    values = c(15, 1)) +
  scale_y_discrete(name = &#39;Treatment Condition&#39;,
                   labels = c(&#39;Anti-Vax Republican&#39;, &#39;Anti-Vax Democrat&#39;,
                              &#39;Republican&#39;, &#39;Democrat&#39;, &#39;Anti-Vax&#39;)) +
  scale_x_continuous(&quot;Estimated Effect on Sympathy&quot;) +
  facet_wrap(~study) +
  labs(title = &#39;Partisanship Structures Sympathy for Covid Victims&#39;,
       caption = &#39;\nNote: Estimated effects were calculated from two separate models (based on partisan identity). \nError bars represent 95/% confidence intervals. Full results table in Appendix E.&#39;) +
  theme(axis.text=element_text(size = 10)) + 
  theme(plot.title = element_text(hjust = 0.5)) + 
  theme(plot.caption = element_text(hjust = 0)) +
  theme(text=element_text(size = 10, family=&quot;serif&quot;))

plot2_sl &lt;- ggplot(df_2, aes(shape = party)) +
  theme_bw() +
  geom_vline(xintercept = 0, linetype = &#39;dashed&#39;) +
  geom_pointrange(aes(x = coef2, y = tr, xmin = lo2,
                         xmax = hi2), size = 1,
                     lwd = 1, position = position_dodge(width = 0.5), fill = &#39;WHITE&#39;) +
  scale_shape_manual(name = &#39;Partisanship&#39;,
                    labels = c(&#39;Democrats&#39;, &#39;Republicans&#39;),
                    values = c(15, 1)) +
  scale_y_discrete(name = &#39;Treatment Condition&#39;,
                   labels = c(&#39;Anti-Vax Republican&#39;, &#39;Anti-Vax Democrat&#39;,
                              &#39;Republican&#39;, &#39;Democrat&#39;, &#39;Anti-Vax&#39;)) +
  scale_x_continuous(&quot;Estimated Effect on Sympathy&quot;) +
  facet_wrap(~study) +
  labs(title = &#39;Partisanship Structures Sympathy for Covid Victims&#39;) +
  theme(axis.text=element_text(size = 10)) + 
  theme(plot.title = element_text(hjust = 0.5)) + 
  theme(plot.caption = element_text(hjust = 0)) +
  theme(text=element_text(size = 10, family=&quot;serif&quot;))

# Testing Group Empathy

## Blame

coef1 &lt;- c(em_death1.1$coefficients[2:6], em_nodeath1.1$coefficients[2:6],
           em_death2.1$coefficients[2:6], em_nodeath2.1$coefficients[2:6])
se1 &lt;- sqrt(diag(vcov(em_death1.1))); se2 &lt;- sqrt(diag(vcov(em_nodeath1.1))); se3 &lt;- sqrt(diag(vcov(em_death2.1))); se4 &lt;- sqrt(diag(vcov(em_nodeath2.1)))

hi1 &lt;- c(coef1[1]+(1.96*se1[2]), coef1[2]+(1.96*se1[3]),
            coef1[3]+(1.96*se1[4]), coef1[4]+(1.96*se1[5]),
            coef1[5]+(1.96*se1[6]),
         coef1[6]+(1.96*se2[2]), coef1[7]+(1.96*se2[3]),
            coef1[8]+(1.96*se2[4]), coef1[9]+(1.96*se2[5]),
            coef1[10]+(1.96*se2[6]),
         coef1[11]+(1.96*se3[2]), coef1[12]+(1.96*se3[3]),
            coef1[13]+(1.96*se3[4]), coef1[14]+(1.96*se3[5]),
            coef1[15]+(1.96*se3[6]),
         coef1[16]+(1.96*se4[2]), coef1[17]+(1.96*se4[3]),
            coef1[18]+(1.96*se4[4]), coef1[19]+(1.96*se4[5]),
            coef1[20]+(1.96*se4[6]))

lo1 &lt;- c(coef1[1]-(1.96*se1[2]), coef1[2]-(1.96*se1[3]),
            coef1[3]-(1.96*se1[4]), coef1[4]-(1.96*se1[5]),
            coef1[5]-(1.96*se1[6]),
         coef1[6]-(1.96*se2[2]), coef1[7]-(1.96*se2[3]),
            coef1[8]-(1.96*se2[4]), coef1[9]-(1.96*se2[5]),
            coef1[10]-(1.96*se2[6]),
         coef1[11]-(1.96*se3[2]), coef1[12]-(1.96*se3[3]),
            coef1[13]-(1.96*se3[4]), coef1[14]-(1.96*se3[5]),
            coef1[15]-(1.96*se3[6]),
         coef1[16]-(1.96*se4[2]), coef1[17]-(1.96*se4[3]),
            coef1[18]-(1.96*se4[4]), coef1[19]-(1.96*se4[5]),
            coef1[20]-(1.96*se4[6]))

study &lt;- c(rep(&#39;Study I&#39;, 10), rep(&#39;Study II&#39;, 10))
party &lt;- rep(c(&#39;No&#39;, &#39;No&#39;, &#39;No&#39;, &#39;No&#39;, &#39;No&#39;,
               &#39;Yes&#39;, &#39;Yes&#39;, &#39;Yes&#39;, &#39;Yes&#39;, &#39;Yes&#39;), 2)
tr &lt;- rep(c(&#39;eAnti-Vax&#39;, &#39;dDemocrat&#39;, &#39;cRepublican&#39;,
            &#39;bAnti-Vax Democrat&#39;, &#39;aAnti-Vax Republican&#39;), 4)

df_3 &lt;- as.data.frame(cbind(coef1, hi1, lo1, party, tr, study))
df_3[, 1:3] &lt;- lapply(df_3[, 1:3], as.numeric)
df_3[, 4:6] &lt;- lapply(df_3[, 4:6], as.factor)
df_3$stupid &lt;- str_c(df_3$party, &#39;, &#39;, df_3$study)

# creating figure G.1
plot3 &lt;- ggplot(df_3, aes(shape = party)) +
  theme_bw() +
  geom_vline(xintercept = 0, linetype = &#39;dashed&#39;) +
  geom_pointrange(aes(x = coef1, y = tr, xmin = lo1,
                         xmax = hi1), size = 1,
                     lwd = 1, position = position_dodge(width = 0.5), fill = &#39;WHITE&#39;) +
  scale_shape_manual(name = &#39;Close Covid Death&#39;,
                    labels = c(&#39;Yes&#39;, &#39;No&#39;),
                    values = c(15, 1)) +
  scale_y_discrete(name = &#39;Treatment Condition&#39;,
                   labels = c(&#39;Anti-Vax Republican&#39;, &#39;Anti-Vax Democrat&#39;,
                              &#39;Republican&#39;, &#39;Democrat&#39;, &#39;Anti-Vax&#39;)) +
  scale_x_continuous(&quot;Estimated Effect on Blame Attributed to Victim&quot;) +
  facet_wrap(~study) +
  labs(title = &#39;Group Empathy and Blame Attribution&#39;,
       caption = &#39;\nNote: Estimated effects were calculated from two separate models (based on partisan identity). \nError bars represent 95/% confidence intervals. Full results table in Appendix E.&#39;) +
  theme(axis.text=element_text(size = 10)) + 
  theme(plot.title = element_text(hjust = 0.5)) + 
  theme(plot.caption = element_text(hjust = 0)) +
  theme(text=element_text(size = 10, family=&quot;serif&quot;))

plot3_sl &lt;- ggplot(df_3, aes(shape = party)) +
  theme_bw() +
  geom_vline(xintercept = 0, linetype = &#39;dashed&#39;) +
  geom_pointrange(aes(x = coef1, y = tr, xmin = lo1,
                         xmax = hi1), size = 1,
                     lwd = 1, position = position_dodge(width = 0.5), fill = &#39;WHITE&#39;) +
  scale_shape_manual(name = &#39;Close Covid Death&#39;,
                    labels = c(&#39;Yes&#39;, &#39;No&#39;),
                    values = c(15, 1)) +
  scale_y_discrete(name = &#39;Treatment Condition&#39;,
                   labels = c(&#39;Anti-Vax Republican&#39;, &#39;Anti-Vax Democrat&#39;,
                              &#39;Republican&#39;, &#39;Democrat&#39;, &#39;Anti-Vax&#39;)) +
  scale_x_continuous(&quot;Estimated Effect on Blame Attributed to Victim&quot;) +
  facet_wrap(~study) +
  labs(title = &#39;Group Empathy and Blame Attribution&#39;) +
  theme(axis.text=element_text(size = 10)) + 
  theme(plot.title = element_text(hjust = 0.5)) + 
  theme(plot.caption = element_text(hjust = 0)) +
  theme(text=element_text(size = 10, family=&quot;serif&quot;))

## Sympathy

coef1 &lt;- c(em_death1.2$coefficients[2:6], em_nodeath1.2$coefficients[2:6],
           em_death2.2$coefficients[2:6], em_nodeath2.2$coefficients[2:6])
se1 &lt;- sqrt(diag(vcov(em_death1.2))); se2 &lt;- sqrt(diag(vcov(em_nodeath1.2))); se3 &lt;- sqrt(diag(vcov(em_death2.2))); se4 &lt;- sqrt(diag(vcov(em_nodeath2.2)))

hi1 &lt;- c(coef1[1]+(1.96*se1[2]), coef1[2]+(1.96*se1[3]),
            coef1[3]+(1.96*se1[4]), coef1[4]+(1.96*se1[5]),
            coef1[5]+(1.96*se1[6]),
         coef1[6]+(1.96*se2[2]), coef1[7]+(1.96*se2[3]),
            coef1[8]+(1.96*se2[4]), coef1[9]+(1.96*se2[5]),
            coef1[10]+(1.96*se2[6]),
         coef1[11]+(1.96*se3[2]), coef1[12]+(1.96*se3[3]),
            coef1[13]+(1.96*se3[4]), coef1[14]+(1.96*se3[5]),
            coef1[15]+(1.96*se3[6]),
         coef1[16]+(1.96*se4[2]), coef1[17]+(1.96*se4[3]),
            coef1[18]+(1.96*se4[4]), coef1[19]+(1.96*se4[5]),
            coef1[20]+(1.96*se4[6]))

lo1 &lt;- c(coef1[1]-(1.96*se1[2]), coef1[2]-(1.96*se1[3]),
            coef1[3]-(1.96*se1[4]), coef1[4]-(1.96*se1[5]),
            coef1[5]-(1.96*se1[6]),
         coef1[6]-(1.96*se2[2]), coef1[7]-(1.96*se2[3]),
            coef1[8]-(1.96*se2[4]), coef1[9]-(1.96*se2[5]),
            coef1[10]-(1.96*se2[6]),
         coef1[11]-(1.96*se3[2]), coef1[12]-(1.96*se3[3]),
            coef1[13]-(1.96*se3[4]), coef1[14]-(1.96*se3[5]),
            coef1[15]-(1.96*se3[6]),
         coef1[16]-(1.96*se4[2]), coef1[17]-(1.96*se4[3]),
            coef1[18]-(1.96*se4[4]), coef1[19]-(1.96*se4[5]),
            coef1[20]-(1.96*se4[6]))

study &lt;- c(rep(&#39;Study I&#39;, 10), rep(&#39;Study II&#39;, 10))
party &lt;- rep(c(&#39;No&#39;, &#39;No&#39;, &#39;No&#39;, &#39;No&#39;, &#39;No&#39;,
               &#39;Yes&#39;, &#39;Yes&#39;, &#39;Yes&#39;, &#39;Yes&#39;, &#39;Yes&#39;), 2)
tr &lt;- rep(c(&#39;eAnti-Vax&#39;, &#39;dDemocrat&#39;, &#39;cRepublican&#39;,
            &#39;bAnti-Vax Democrat&#39;, &#39;aAnti-Vax Republican&#39;), 4)

df_4 &lt;- as.data.frame(cbind(coef1, hi1, lo1, party, tr, study))
df_4[, 1:3] &lt;- lapply(df_4[, 1:3], as.numeric)
df_4[, 4:6] &lt;- lapply(df_4[, 4:6], as.factor)
df_4$stupid &lt;- str_c(df_4$party, &#39;, &#39;, df_4$study)

# creating figure G.2
plot4 &lt;- ggplot(df_4, aes(shape = party)) +
  theme_bw() +
  geom_vline(xintercept = 0, linetype = &#39;dashed&#39;) +
  geom_pointrange(aes(x = coef1, y = tr, xmin = lo1,
                         xmax = hi1), size = 1,
                     lwd = 1, position = position_dodge(width = 0.5), fill = &#39;WHITE&#39;) +
  scale_shape_manual(name = &#39;Close Covid Death&#39;,
                    labels = c(&#39;Yes&#39;, &#39;No&#39;),
                    values = c(15, 1)) +
  scale_y_discrete(name = &#39;Treatment Condition&#39;,
                   labels = c(&#39;Anti-Vax Republican&#39;, &#39;Anti-Vax Democrat&#39;,
                              &#39;Republican&#39;, &#39;Democrat&#39;, &#39;Anti-Vax&#39;)) +
  scale_x_continuous(&quot;Estimated Effect on Sympathy for Victim&quot;) +
  facet_wrap(~study) +
  labs(title = &#39;Group Empathy and Sympathy&#39;,
       caption = &#39;\nNote: Estimated effects were calculated from two separate models (based on partisan identity). \nError bars represent 95/% confidence intervals. Full results table in Appendix E.&#39;) +
  theme(axis.text=element_text(size = 10)) + 
  theme(plot.title = element_text(hjust = 0.5)) + 
  theme(plot.caption = element_text(hjust = 0)) +
  theme(text=element_text(size = 10, family=&quot;serif&quot;))

plot4_sl &lt;- ggplot(df_4, aes(shape = party)) +
  theme_bw() +
  geom_vline(xintercept = 0, linetype = &#39;dashed&#39;) +
  geom_pointrange(aes(x = coef1, y = tr, xmin = lo1,
                         xmax = hi1), size = 1,
                     lwd = 1, position = position_dodge(width = 0.5), fill = &#39;WHITE&#39;) +
  scale_shape_manual(name = &#39;Close Covid Death&#39;,
                    labels = c(&#39;Yes&#39;, &#39;No&#39;),
                    values = c(15, 1)) +
  scale_y_discrete(name = &#39;Treatment Condition&#39;,
                   labels = c(&#39;Anti-Vax Republican&#39;, &#39;Anti-Vax Democrat&#39;,
                              &#39;Republican&#39;, &#39;Democrat&#39;, &#39;Anti-Vax&#39;)) +
  scale_x_continuous(&quot;Estimated Effect on Sympathy for Victim&quot;) +
  facet_wrap(~study) +
  labs(title = &#39;Group Empathy and Sympathy&#39;) +
  theme(axis.text=element_text(size = 10)) + 
  theme(plot.title = element_text(hjust = 0.5)) + 
  theme(plot.caption = element_text(hjust = 0)) +
  theme(text=element_text(size = 10, family=&quot;serif&quot;))

plot1; plot2; plot3; plot4</code></pre>
<p><img 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" 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CB5AgRAk2fLlhFAAAEEEEAAAQQQQAABBBBAAAEEEEAgjwUIgObxCWD3CCCAAAIIIIAAAggggAACCCCAAAIIIJA8AQKgybNlywgggAACCCCAAAIIIIAAAggggAACCCCQxwIEQPP4BLB7BBBAAAEEEEAAAQQQQAABBBBAAAEEEEieAAHQ5NmyZQQQQAABBBBAAAEEEEAAAQQQQAABBBDIYwECoHl8Atg9AggggAACCCCAAAIIIIAAAggggAACCCRPgABo8mzZMgIIIIAAAggggAACCCCAAAIIIIAAAgjksUDRPN4/u0cAAQQQQAABBDIVOP300+2dd95xy9SuXTvTZZmJAAIIIIBAUALt2rULff4cfvjhQW2W7SCAAAII5IEAAdA8QGeXCCCAAAIIIBC/wP7772+NGjVyK5QuXTr+FVkSAQQQQACBHAhUrFiRz58c+LEqAgggkEoCNIFPpbNBXRBAAAEEEEAAAQQQQAABBBBAAAEEEEAgUAECoIFysjEEEEAAAQQQQAABBBBAAAEEEEAAAQQQSCUBAqCpdDaoCwIIIIAAAggggAACCCCAAAIIIIAAAggEKkAANFBONoYAAggggAACCCCAAAIIIIAAAggggAACqSRAADSVzgZ1QQABBBBAAAEEEEAAAQQQQAABBBBAAIFABQiABsrJxhBAAAEEEEAAAQQQQAABBBBAAAEEEEAglQQIgKbS2aAuCCCAAAIIIIAAAggggAACCCCAAAIIIBCoAAHQQDnZGAIIIIAAAggggAACCCCAAAIIIIAAAgikkgAB0FQ6G9QFAQQQQAABBBBAAAEEEEAAAQQQQAABBAIVKBro1tgYAggggAACCCAQsMCiRYtsypQpbqstWrSwE088MeA9sDkEEEAAAQT2Fli8eLFNnjzZzWjevLk1a9Zs74WYggACCCCQFgIEQNPiNFFJBBBAAAEECq7AF198YbfffrsDGDhwIAHQgvtS4MgRQACBXBWYPXt26PPnrrvuIgCaq/rsDAEEEAhWgCbwwXqyNQQQQAABBBBAAAEEEEAAAQQQQAABBBBIIQECoCl0MqgKAggggAACCCCAAAIIIIAAAggggAACCAQrQAA0WE+2hgACCCCAAAIIIIAAAggggAACCCCAAAIpJEAANIVOBlVBAAEEEEAAAQQQQAABBBBAAAEEEEAAgWAFCIAG68nWEEAAAQQQQAABBBBAAAEEEEAAAQQQQCCFBAiAptDJoCoIIIAAAggggAACCCCAAAIIIIAAAgggEKwAAdBgPdkaAggggAACCCCAAAIIIIAAAggggAACCKSQAAHQFDoZVAUBBBBAAAEEEEAAAQQQQAABBBBAAAEEghUgABqsJ1tDAAEEEEAAAQQQQAABBBBAAAEEEEAAgRQSKJpCdaEqCCCAAAIIIIDAXgKFCxe2ffbZx00vUqTIXvOZgAACCCCAQDIE+PxJhirbRAABBPJGgABo3rizVwQQQAABBBCIU+CCCy6wDh06uKVLly4d51oshgACCCCAQM4EzjvvPDvttNPcRvj8yZklayOAAAJ5LUAT+Lw+A+wfAQQQQAABBBBAAAEEEEAAAQQQQAABBJImQAA0abRsGAEEEEAAAQQQQAABBBBAAAEEEEAAAQTyWoAAaF6fAfaPAAIIIIAAAggggAACCCCAAAIIIIAAAkkTIACaNFo2jAACCCCAAAIIIIAAAggggAACCCCAAAJ5LUAANK/PAPtHAAEEEEAAAQQQQAABBBBAAAEEEEAAgaQJEABNGi0bRgABBBBAAAEEEEAAAQQQQAABBBBAAIG8Fiia1xVg/wgggAACCCAQn8Dq1att8eLFtnHjRtuzZ4+VKVPGqlevbjVr1rQiRYrEtxGWQgABBBBAAAEE/r/A9u3bbdGiRbZy5UrbunWrlShRwipXrmxHHHGE+54BFAIIIJBfBAiA5pczyXEggAACCORLgRkzZtiLL75okydPtl9++SXmMZYuXdpatWpl3bp1sy5duljx4sVjLsdEBBBAAAEEEEBgzZo1NmbMGHv11Vdt9uzZ7qJqtEqhQoWsQYMG1rFjR7v44outVq1a0YvwHAEEEEgrAZrAp9XporIIIIAAAgVF4MMPP7Rjjz3WWrZsac8880yGwU95/PHHH/bWW2/ZBRdcYDVq1LBHH3005o+ZgmLHcSKAAAIIIIDA3gLr16+36667zrUeueWWW+zzzz/P8PvCP//8Y/PmzbN77rnHateubeedd54tX758740yBQEEEEgTAQKgaXKiqCYCCCCAQMEQ2LZtm1122WXWunVrl5WR3aNWVseNN95oTZs2tR9//DG7q7M8AggggAACCORDAbUkadiwoT3++OO2c+fObB3h33//bS+99JLVrVvXrZ+tlVkYAQQQSBEBmsCnyImgGggggAACCKiPz9NOO81lXERr7LPPPnb88cdbo0aN7KCDDnJ9fm7YsMG+//57++STT1y/oOHrzJ071wVB9YPl6KOPDp+Vdo8//fRTe/rpp129lYHStWvXtDsGKowAAgggkH4CM2fOtCeffNJVXJ89+gxKxzJo0CDr379/zKqr5UizZs3ssMMOM3WpowuxyvScNWuWLViwIGId9ReqDNKvvvrKtU4pWpRwQgQQTxBAIKUFeMdK6dND5RBAAAEECorA77//7vrx1EAE4aVChQrWt29fu/TSS02PYxUNiPT222/b3Xffbd9++21oETWNP+uss1w/X507dw5NT7cHS5cudf2Uqd7pHsxNN3vqiwACCBRkAQUC1U+mSv369dOS4s477zQFQKPL2Wefbf/+97+tSZMm0bNCzzXw4kMPPWTPPfdcRFP5559/3nbs2GHjxo0z9RVKQQABBNJBgCbw6XCWqCMCCCCAQL4WUACzU6dObhTW8AO95JJLbMmSJdanT58Mg59aXhkYWn/OnDn21FNP2b777hvazO7du13wVD9iKAgggAACCCBQcAQ0iGJ08PPQQw81tax45ZVXMg1+Sunwww933yvmz59vjRs3joCbMGGCKbhKQQABBNJFgABoupwp6okAAgggkG8FBg4caGpm54uyKR5++GEbNWqUlStXzk/O8r5w4cJ21VVX2UcffWSVKlUKLb9161a78MIL7a+//gpN4wECCCCAAAII5F+BZcuW2TXXXBNxgBpcUaO+q8l7dsqRRx7pgqZdunSJWO2+++5z3fBETOQJAgggkKICBEBT9MRQLQQQQACBgiGgDM/7778/4mDvuusuu+mmmyKmZeeJ+gp94403IjJBv/nmG5fFkZ3tsCwCCCCAAAIIpKeAvkfoAqgv6uvztddeswMOOMBPytZ9iRIlbPz48XbSSSeF1tNI8ddee61pkCQKAgggkOoCBEBT/QxRPwQQQACBfC1w77332q5du0LHqEGQ1JdnTssJJ5xgw4YNi9jM4MGDI/YVMZMnCCCAAAIIIJAvBHTRUxdCfVFXOWpVUr58eT8poXsNyDhp0qSIIOp3331nEydOTGh7rIQAAgjkpgAB0NzUZl8IIIAAAgiECWzatMllU/hJRYoUcYMNBDWgwJVXXml16tTxm7eVK1dG/CAKzeABAggggAACCOQbAT9yvT+gq6++2mrXru2f5uh+//333+tC7RNPPJGjbbIyAgggkBsCBEBzQ5l9IIAAAgggEENA2RkaRdWX7t27RwQs/fRE7xVQjR6gQIMWUBBAAAEEEEAgfwqov29lafpSrFgxu+OOO/zTQO6vuOIKq1q1amhbGlRp1apVoec8QAABBFJRoGgqVoo6IYAAAgggkFcC+uGgUdl37tzpbqqHHmuAoaDL+++/H7HJbt26hfYZMSMHT0499VQrVapUqB+w6dOnu6BrUFmmmVVNZmouR0EAAQQQQCC/CKi/y927d6fs4Xz11VemFia+tGnTxsqUKWNr164N9PuMBkQaPny42436Ap06darpQm6yiroL0vcxFX2/8I/j3Z+6AdCFYQoCCBRcAQKgBffcc+QIIIAAAjEENCBR0JkSMXYTc1KHDh1iTg9y4saNG00DGeRG0Wizs2bNyo1dsQ8EEEAAAQRyRWDkyJGmDMh0Ke+++667EJrs+vbs2dN0S9XSp08fe+CBB1K1etQLAQRyQSD4dJZcqDS7QAABBBBAAAEEEEAAAQQQQAABBBBAAAEE4hEgAzQeJZZBAAEEEEAAgTwTqFKlijVv3tzt/9BDD82zerBjBBBAAIGCJVC5cuXQ50+1atUK1sFztAgggEA+EyAAms9OKIeDAAIIIIBAfhNo27atqTm9SunSpfPb4XE8CCCAAAIpKtC6dWs75phjXO34/EnRk0S1EEAAgTgFaAIfJxSLIYAAAggggAACCCCAAAIIIIAAAggggED6CZABmn7njBojgAACCCRRoG7dunbuuee6EV41GryKRjLX6KFBlw8//NDWrVsX2qwGQQp6gCKNav/BBx/Y5s2bQ/vp1KmTFStWLPQ8WQ8OO+ywZG2a7SKAAAIIIJAnAjVr1nTfE/Jk53Hs9JdffrHPP/88tOSRRx5p9evXd6OmB/l9ZtmyZTZ79uzQfrQPfYdKVtH3GY0Er6LR3LP7PaZhw4bJqhrbRQCBNBEI/tdcmhw41UQAAQQQQCCWgIKDum3ZssW2bt3qFilXrlzggUlt+LbbbrOhQ4eGqqEAaNAjy/78889Wo0aN0D70Q+i1114LPecBAggggAACCMQvcMopp5huqVoUAA3vL1uBwgkTJtjatWstyO8zulgcHgAdNmyYtWnTJmksCn6uX7/ebV8Xi3UsFAQQQCA7AjSBz44WyyKAAAIIIBCgQPv27SO29tRTT9k///wTMS2nT5555pmIbUbvM6fbZ30EEEAAAQQQSB2BQw45JCIT89tvv7XPPvss0Ar++uuv9uabb4a2ud9++1mzZs1Cz3mAAAIIpKIAAdBUPCvUCQEEEECgQAicdNJJph8qvnzzzTf20ksv+ac5vv/tt9/skUceidjOhRdeGPGcJwgggAACCCCQvwQuuuiiiAPq27dvxPOcPrnrrrtck3q/na5duyalpYzfPvcIIIBAEAIEQINQZBsIIIAAAggkIFC4cGHr3bt3xJp9+vQxBS5zWpRJ+q9//SvUjF/bO/nkk61x48Y53TTrI4AAAggggEAKC1x55ZUWPmr9zJkzbfTo0YHUWP2Kv/DCC6FtFSpUaK/vMqGZPEAAAQRSSIAAaAqdDKqCAAIIIFDwBK6++mrTgAq+rFy50jp37mw7duzwkxK679+/v73yyiuhdfUDZciQIaHnPEAAAQQQOX+iOwAAQABJREFUQACB/ClQvnx5u/322yMOrl+/fvbJJ59ETMvuk0WLFrkBoP7+++/Qqso21QBIFAQQQCDVBRgEKdXPEPWzTz/91D766CML/6D1LPpBX6ZMGatQoYK71alTJyKQ4JfjHgEEEEhVgeLFi9vTTz9t7dq1C73PafRWZWtqsKIDDzwwW1XXKKnKIo1u+n7dddfZsccem61tsTACCCCAAAIIpKeAvgu8/PLLNnfuXHcAe/bscYM8KntTAxhlt+g3WZcuXWzjxo2hVatUqWIPPPBA6DkPEEAAgVQWIAM0lc8OdXMCzZs3t5tvvtn0of3++++723fffeeuNDZs2ND23Xdf09XIkSNH2sUXX2w9evSwESNG2B9//IEgAgggkBYCGjX17rvvjqjrF198YU2aNLExY8ZEDGIUsVDUk6+//toFTqODn8ccc4wNHjw4ammeIoAAAggggEB+FdDo7wqAVqxYMXSIal3SrVs3U+uTNWvWhKZn9kC/qdTnZ+vWrd1I8n5ZbX/8+PFWqVIlP4l7BBBAIKUFCICm9Omhcl6gRIkSFj5ycdu2be3ss8+2jh07ug/we+65xyZMmGA33XSTbdu2zV588UXTQB8zZszwm+A+QIHly5cHuDU2hQACEtCPC/XZGV5WrVrl3suOPvpoe+KJJyzW/96mTZtcpqjeE5s2bbrX+95RRx3l+urSDxUKAggggAACCBQcgcMOO8zeeecd12Iu/KjV8kTzbrzxRtfabteuXeGzXYsUDcx45513Wq1atUy/tcKX2WeffWzs2LGmwRwpCCCAQLoIEABNlzNFPSM68taHbnTRNAUAxo0b50ZVXr9+vd1xxx322WefRS/K8xwIKCstvOPzHGyKVRFAIErg//7v/9yPDA2OFF6+/fZbFxytUaOGqV+vunXrWoMGDezggw92z/Xep+byGvgovJx66qk2bdo0t0z49HR7rL5MFdzV7dlnn0236lNfBBBAAIE0FXjjjTdCnz8KGqZjOe644+yj/3QnVrVq1Yjqb9261R599FFr0aKFlSpVygU6dcG1du3a7rlaoQwaNMh+//33iPX0PeTtt9+2c845J2I6TxBAAIFUF4j8hZXqtaV+BVpA/X3GU0qWLGkDBw40ZTspGDBgwABTFhUl5wJqKqMrwNFBlpxvmS0ggIAX0CAFGmFVmRmxijI+Fy5caPPnz8/wvU0/ZNQn13vvvWdly5aNtZm0mqYfaRocSje6N0mrU0dlEUAAgbQWCP/82bJlS9oeiy6aqisxdRUWq6irsaVLl9q8efNs8eLFGQ7EqP7K58yZY7rASkEAAQTSTYAAaLqdMeobl4ACB+rbRmX79u32+uuvZ7ieOvLWj+qsgnoahElfgnzR8gqshjcH8fM0TdvUYCTxFF1Z1bayqkP4tlQXBUB+/vln1z9q+Lzwx3/++afppqLtL1u2bK/96AvdggULXLZsRsFiBT/VxYD/8qdtZae+4XXiMQIIZC6gAZD0P/nYY4+Zsj7jLaVLl7YbbrjBfvzxRzcQUnQmabzbYTkEEEAAAQQQyF8CuiCqVhRqzaVuxIoUKRL3ASpLVE3pJ0+ebNWqVYt7PRZEAAEEUkmAUeBT6WxQl0AFNHiSmnWoKAtKAdHwYID6DNWHeNGiRd0VTzWhv+iii1zH4JrmiwKGU6ZMsalTp9oll1xiGqxE2/34449dYFGZpr1797YzzjjDZSapecy7775ru3fvNvVdqo7GL7vsMr+50L36Kn388cfdlxCNXq/A46+//mqNGze2Xr16ZTjys0Zy1D4OOuggK1eunGvSouO69tpr3eAn2oGCtVpO9f7www/t/vvvN2XQDhkyxO1HNhoQRXV88sknTSNO64vQfvvtZ6NHj3bH8fDDD4fqoADqbbfdFuosXceu41VRtq0GWKEggECwAhrgTSO3q19QdeWh/+dZs2a5zIwNGza4Cx9lypRxAVINCKfBCU4//XRTFjwFAQQQQAABBBCIJaAm8Wrav3r1anvzzTfdbxoNMKvkDf0+KV68uFWuXNn0+0SBzw4dOmTYKiXW9pmGAAIIpKrA/6I8qVpD6oVAggIHHnigC0AqA1RNRhUw2H///V1wUM24Fbh87rnn3NXPzZs3u6ypp556ylasWGF9+/Z1e1UTD13tVJMRBRV/+OEHmz59ugv4qWn9R//pT+ett96yBx980PXFp6uq9erVc4FGNVHV9p9//nk78sgjrVmzZqEjUaCzT58+blRG9aepwKOKrsgq0KhOxzUidPg6mj9x4kSbNGmSa9rqr76ecsopLrir5dXs9dhjj3X98mg5BW9VfvrpJ1dvBU19oFXTNVK0vvjcd9997guOprVq1cp90VG2p/pTVeBU9VM9tZyCyQoCq3/VrMoFF1wQyj6NXlaZagrARvcrFL1cdp7rHOkW5Dazs/8glg3PGlZ2crxdPwSx7yC3ofOgor5407WEnwtlPvtM6rw4niOOOMJ0y6ooMzw8U13L+0ztdH49adRaX5Rhn87/4zoONePPy9eTt0zk3r+etK7OS16ci507d8bdwiLWMep/W809g6y73vNkE+Q2Y9U9mdPC3/PS+f2Cz59kvkqyt23/fpHOr6fwz5+gv7dmTzOYpcM/f5TwoT7EdcuqpMJ7m389qa55+fnj32OyMmM+AgikngAB0NQ7J9QoIAEFjmrWrGm6oqmydu1aFwBVAG/27NmuWbxv+qEmIeeff77r31LZm+rfRpmYjRo1crclS5a4IKICiUOHDnWZl9qmBuRQUFR95SijctiwYS4zU/MUiFSwUdv75JNPIoKZysjUVVcFFH3wU+scf/zx1rNnTxc4VbbmmDFjQv33qUmrBkhR4NUHP7WOAq4anVF1VNan9qtsTmV5nnXWWVrEZboqa1RZrvJQIFhF/QyqhNdBgckKFSq4bFQFUGUYXeINyqnOvsl89DbUybqKfoQGXZKxzaDrGM/2wn+MxrN8Ki6TX86Fvuym+xfedH49hf/o0eN0f12l87kIf5/Jq3OR0/9F/3pKxusoGdsMN8+tx/nhNZpfzgWfP7n1qo+9H/9+obk6F+n+usoP/9s6F3n5+RP+mlBdKAggkD4C9AGaPueKmiYgoCbivigTVFkjzzzzjNWvX9/1namgob+FDxSiDMzwcsABB7inajISvk0FArUtFXUurgzL8KLApIqCr74oa1SBSi0bK7jYuXNnl5WqrNXw0dbVJF11VIZmdOnfv79dddVVEaMxKoipJiwqnTp1csFPPVbAVM1aVC6//HJr2bKlHXXUUe65/6PsWJWMsvfiDYD67XGPAAIIIIAAAggggAACCCCAAAII5JUAGaB5Jc9+c0VAGZu+VKxY0fX1qaCeBvRRX5fRRc3JVcqXLx8xK7OAn5qdZ1T8dtSfji8KfqpEB0v9fAU5lVmq5vA+e1VNh77//nsXvIxVl+rVq5tu0UV9AaqJiA/gRs/v2rWr6eaLslmVsaruAlRiDfDkl43nfsaMGaHmt9HLK2ir86OuCoIqatYjax/gDWq7ubkdBb59cyu9ZpW1m45F50HZv3rthfe9m07HoteTb06uCx/+gkI6HYPqqv9j/U+n8+tJ/Sn7or5Rg3zf8NvNjXtl3qgZod7nw48pN/Yd1D7860nb0zGEXzwMah9ZbUf79S04slo21nytq6afQb6O/PtFkNuMVfdkTuPzJ5m62du2fz1prfzwfsHnT/bOfzKW5vMnGNWcfv4EUwu2ggACiQoQAE1UjvVSXkD9ev7222+unvqwUsBRAwKpqGn7jTfe6B7n9I/Ploy1HR+sDG+ut3TpUreoBi/JqCiYqQDo8uXL3SL+XscRdFEzDg1qpM7Q69ataxdffLF9+eWXriP0jPbljyuj+X56ZgEjvw1/79fJyb225W852U6qrJvOx+LPazofQ/TrwB9T9PRUf+7rzbnI+zOVH86FPwavGf3cT0/le19nfx9EXbUtfwtie3m9jXQ+Fn9e88Mx6HWQH44jnY8h+n/Rv76ip6f6c1/vdD4X/hi8dfRzP517BBBAICMBAqAZyTA97QUWLVoUOoYTTzzRDXrkB53QQEe5WcI/oH3Wyrp16zKsgs/Y9JlzPiPzl19+yXCdRGYoQ+/WW291g3E88MADVqVKlbg2E348ca3AQggggEAuC5x55pkW/jmQrN2rGxENgEdBAAEEEEhtAQ38qUFIs1OUUOGL+uIfP368f5rpvQZJjWfwwkw3wkwEEEAAgUAFCIAGysnGUkVAGZevvfaaq46Cdd27d3ePq1at6u41Qrua6KqJeKyi4KQGA1JTy6BLjRo17Kuvvso0w9I3PdeyKr7eymhVs/HDDjssZrXUz2m8WaLqD7VXr15uACd9mYs3+KkdEwCNyc9EBBBIIYGff/7Zvb8lu0q+r+dk74ftI4AAAgjkTEAX/jVwaaJFCQk+KSGrbeh7NgUBBBBAILUECqdWdagNAsEIPPLII6b+J1XOPffc0BVY/VBVVqX6VpowYULMnenL0aBBg5LWb6EfcEj9wC1YsCBmHTRPpWHDhu7+0EMPDfV3pkGcYo0+qC9k2clCmjdvnvsSqBHhfYDV7SyOP/llBMk4DpVFEEAgBQT0XnjzzTe72/HHH58CNaIKCCCAAAIIIIAAAgggkE4CBEDT6WwV8Lr6gWEyY9izZ48LAvrsz44dO9q1114bWqVSpUrWtm1b93zMmDH2+uuvh+bpgTIs77jjDmvXrl3E4DOxAo5+RX+F12dt+um69+v5e03TKO4+sKl+N6OLlv3888/daPPnn3++m62szm7durnHM2fOdAFav19NVJP+AQMGhDJd3YL/+eP3u3v3bj8pdO+vgCvb9dtvvw1NV2B05cqV7nm0uTqxV/EBWj0O799UzykIIIBA0ALhAdATTjgh6M2zPQQQQAABBBBAAAEEEMjnAjSBz+cnOD8d3pIlS0KHM3/+fNPI6GrCrgDe2rVrXbPyd955xzTKu5pzX3HFFaFgZ2jF/zy49NJL3bJa7qGHHrJJkybZkUceacuWLXP9xZ1zzjl22mmnhVZRUFUjsKvE6oNz9erVbp5GUI8uvs5aT0FIjeit5uPqd/P666+3yZMnW5MmTax9+/ahVTUKuwKMGiV9v/32C03v2bOnC4yqnlOnTrXp06db7dq13QjPCoDedtttpmxOX2QiI5XZs2dbixYt/Cx3r2P2ZciQIe6YFfhUYLV+/fouO/WVV15x21DWqvoxOuSQQ9wqc+bMsffff9/1q6ruBK6++mq/Ke4RQAABBBBAAAEEEEAAAQQQQACBlBIgAJpSp4PKxBL47LPPTIFNZT76MmvWLFN2p4pGGteI6uXKlTMNRqER3ps3bx6RwenX071Gg3/66aftzjvvNAXv1E+cburzs2/fvnb66aeHFldmpLJJN23a5KYpM3Ps2LF2yimnuMDjq6++6kZQ18xVq1aZAokdOnRwTcpHjhzpgoSat3XrVrftzp07W7NmzUxN2p9//nm3/P333++OTfXWCPEKLj755JOhZvtaX0XHqebv6oBdQVJlnH733XdWrVo1GzZsmPmm9Vp24sSJNm3aND10RcuriXynTp3smGOOcdMUeL3uuuvcsgrQfvTRRy4Q26VLF5P54MGDXVBYx65Aq0rr1q1Nx/zjjz/aPffc4461d+/ebh5/EEAAAQQQQAABBBBAAAEEEEAAgVQUIACaimeFOkUIKGCoW5ClcuXKNmLECFN/nxpUSE271Q+mH6Hd76tBgwam29133+0nRdzfdNNNplusktk8LV++fHlT8FMBRo1UrEGZevTo4fq4i7U9TdOgTOoH78Ybb3RB2woVKrjtRC+vfk91y6qoWb0yXlUH37xd6yhbtGnTpla0aFF389vR/hWEVcBYyytoTEEAAQQQQAABBBBAAAEEEEAAAQRSWYAAaCqfHeqWdAFljjZu3Djp+8lsB8pcPfbYYzNbZK95CkwGNfKwgr7hwU+/M2Wcxipqwl+9evVYs5iGAAIIIIAAAggggAACCCCAAAIIpJwAgyCl3CmhQggggAACCCCAAAIIIIAAAggggAACCCAQlAAB0KAk2Q4CCCCAAAIIIIAAAggggAACCCCAAAIIpJwATeBT7pRQIQQQQAABBAqGwJdffmka1C6rokHftm/f7hZT9xzqjzirsn79+qwWCWS++kR+7LHH4trW33//bX/88YeVKFHCihUrluU6Gniubt26WS7HAggggECqC0yaNMlWr16dp9WcPXt2ru1/3LhxoYFSc22nUTvSAKmtWrWKmspTBBBAoOAKEAAtuOeeI0cAAQQQQCBPBd59910bMGBAntYhpztfuHCh3XDDDTndTMz1NegcAdCYNExEAIE0Exg2bJjNnDkzzWqdeHU10Glel6uvvpoAaF6fBPaPAAIpJUAT+JQ6HVQGAQQQQAABBBBAAAEEEEAAAQQQQAABBIIUIAAapCbbQgABBBBAAAEEEEAAAQQQQAABBBBAAIGUEiAAmlKng8oggAACCCCAAAIIIIAAAggggAACCCCAQJACBECD1GRbCCCAAAIIIIAAAggggAACCCCAAAIIIJBSAgyClFKng8oggAACCCBQcAQuvvhia9GiRZYH/NZbb9nw4cPdclrnwgsvzHKdyy+/3JYvX57lcjldoGnTpjZ48OC4NvPXX3/Zpk2bbL/99otrJPs6derEtV0WQgABBFJd4NFHH3Xvf3lZzw8++MCGDBmSK1UYMWKE1axZM1f2ldFOqlatmtEspiOAAAIFUoAAaIE87Rw0AggggAACeS9Qo0YN0y2rsnjx4tAitWrVstatW4eeZ/RAQcbcKBUrVoyrPqqLAqBr1661cuXKWYkSJXKjeuwDAQQQSAmBJk2a5Hk9Vq9enWt1OO6446xBgwa5tj92hAACCCCQtQBN4LM2YgkEEEAAAQQQQAABBBBAAAEEEEAAAQQQSFMBAqBpeuKoNgIIIIAAAggggAACCCCAAAIIIIAAAghkLZCnAdD+/ftnXUOWQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEhRIWh+gK1eutIULF9qff/65V9X++ecfmz9/vj3xxBNGEHQvHiYggAACCCCAAAIIIIAAAggggAACCCCAQEACgQdAf/jhB+vatastWLAgoCqyGQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIDGBwAOg7dq1sxUrVrjalCxZ0sqWLbtXzfbs2WMbNmxwo6HuNZMJCCCAAAIIIIAAAggggAACCCCAAAIIIIBAQAKBBkCV9angZ82aNe3ll1+2Bg0aWNGisXexZcsWa9myZUCHwWYQQAABBBBAAAEEEEAAAQQQQAABBBBAAIG9BWJHJ/deLq4p1apVs2LFitktt9xijRs3znSdMmXK2H333ZfpMsxEAAEEEEAAAQTatm1rEydOdBD16tUDBAEEEEAAAQQQQAABBBDIlkCgAdDSpUtb69atbdu2bXFV4oQTTohrORZCAAEEEEAAgYIrUKVKFWvevLkD0HeNeMopp5xitWrVimfRHC2T1QXfHG2clRFAAAEEAhM45JBD7KyzzsrW9tR1m24qatmYUevG6I3G6gYuehmeI4AAAgjkrkCgAVBV/bHHHrOePXvaNddcYyVKlMj0aAYPHmxDhw7NdBlmIoAAAggggAAC2RUYPnx4dldheQQQQACBfCxw0kknmW7ZKUrs2bx5s1tFF+D222+/7KzOsggggAACKSQQaAB006ZNbvT3Ro0aWefOna19+/YZHqpGix8/fjwB0AyFmIEAAggggAACCCCAAAIIIIAAAggggAACORUINABauHBh69atm+3cudPVa8qUKTmtH+sjgAACCCCAAAIIIIAAAggggAACCCCAAAIJCwQaANXARhqo4O2337aKFStahQoVYlbsn3/+sbVr15pGgqcggAACCCCAAAIIIIAAAggggAACCCCAAALJEgg0AKpKdunSxSpVqmQjR47MtM7qTLpNmzaZLsNMBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgJwKBB0A7dOhgderUybJOGkFv0KBBWS7HAggggAACCCCAAAIIIIAAAggggAACCCCAQKICgQdA1fRdt3hK8+bN41mMZRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgYQEAg+AhtdC/XzOmTPHvv/+eytUqJDVq1fPmjRpkmHfoOHr8hgBBBBAAAEEEEAAAQQQQAABBBBAAAEEEMipQFICoLt377YhQ4a4Ju67du2KqGOpUqWsX79+1rt3bytWrFjEPJ4ggAACCCCAAAIIIIAAAggggAACCCCAAAJBCgQeANUI72eeeaZNnTo1op7lypWznTt32tatW61v37727rvv2ocffmjqC5SCAAIIIIAAAghkJLBs2TKbPn26m33cccdZ06ZNM1qU6QgggAACCAQmsHz5cvebVRvk8ycwVjaEAAII5IlA4aD3OmLECBf8POCAA+ymm26yL7/80v7880/buHGjbdu2zdavX28vvPCCe65AKAUBBBBAAAEEEMhMYMaMGXb99de72+TJkzNblHkIIIAAAggEJjBz5szQ58/bb78d2HbZEAIIIIBA7gsEnn45dOhQa9Wqlb388su2//7773VEFSpUsIsuusg6depkJ598sguKlixZcq/lmIAAAggggAACCCCAAAIIIIAAAggggAACCORUINAMUJ/pOWXKlJjBz/DKlilTxnr06GFz584Nn8xjBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgMIFAA6ArVqyw1q1bxz240ebNm+3nn38O7GDYEAIIIIAAAggggAACCCCAAAIIIIAAAgggEC4QaAC0Vq1atnjx4vDtZ/hYgyGpmXz9+vUzXIYZCCCAAAIIIIAAAggggAACCCCAAAIIIIBATgQCDYDuu+++VqhQIXv11VczrdOSJUusWbNmtmbNGqtbt26myzITAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIVCDwQZAeeOABa9eunXXt2tU6duxoNWvWtIoVK9qqVavsp59+sokTJ9oHH3xgf//9t40bN86KFCmSaN1ZDwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyFQg8ABomzZtrF+/fjZw4EB76aWXMtx5r169rHv37hnOZwYCCCCAAAIIIIAAAggggAACCCCAAAIIIJBTgUCbwPvKDBgwwKZPnx6zf8969eq5DNBhw4b5xblHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSSIhB4BqivZatWrWz+/Pm2YcMGW7BggWvqrgGPypYt6xfhHgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQSKpAUjJAw2tcoUIFa9mypRv0KDr4OXr06PBFeYwAAggggAACCCCAAAIIIIAAAggggAACCAQqkLQM0KxquWPHDhs7dqxddNFFWS3KfAQQQAABBBAowAIXXHCBnXbaaU6gfPnyBViCQ0cAAQQQyE2B8847z9q2bet2yedPbsqzLwQQQCB4gYQDoBrEaO7cuTZq1CirUaOGq9nq1atdpmdW1dy9e7etW7fOFASlIIAAAggggAACmQkULlzYihUr5hbRYwoCCCCAAAK5IcDnT24osw8EEEAgdwQSCoCuX7/ehg8f7mr4xhtvmIKhKlWqVLGDDjrIPvvsM/ecPwgggAACCCCAAAIIIIAAAggggAACCCCAQF4KJBQArVixot1zzz02b948O/fccyPq36NHD1uzZo317t07wwGPlAH666+/2sMPPxyxLk8QQAABBBBAAAEEEEAAAQQQQAABBBBAAIEgBRIKgKoC/fr1i1mPLl262J49e+zaa6+NOT984pYtW8Kf8hgBBBBAAAEEEEAAAQQQQAABBBBAAAEEEAhUIPCOtCpXrmzt27ePq5Ldu3ePazkWQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEhFIOAM0s53Vrl07s9n2999/2/z5861Ro0aZLsdMBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgJwKBZ4C2bds2y/poNL0XX3zRZs+eneWyLIAAAggggAACCCCAAAIIIIAAAggggAACCCQqEGgA9J9//rGvv/46rrp07NjROnfubFqHggACCCCAAAIIIIAAAggggAACCCCAAAIIJEMgx03gN2/e7EZ9V+UUzFTz9uXLl1uhQoVi3jRA0sqVK23o0KHufuHChVa3bt1kHBvbRAABBBBAAAEEEEAAAQQQQAABBBBAAIECLpDjAOjWrVvt8ssvt08//TREWaNGjdDjzB4UKVLEDj744MwWYR4CCCCAAAIIIIAAAggggAACCCCAAAIIIJCwQI6bwB900EE2ffp069OnT9yVUHZotWrVbMyYMVa2bNm412NBBBBAAAEEECh4Al988YXdeuut7jZt2rSCB8ARI4AAAgjkiYDGrPCfP1OnTs2TOrBTBBBAAIFgBHKcAapqFC1a1B544AGrUKGC3X///TZ37twMa6fgZ+XKla148eIZLsMMBBBAAAEEEEDACyxatMhdNNXz2rVr21lnneVncY8AAggggEDSBBYvXhz6/KlZs6adffbZSdsXG0YAAQQQSK5AIAFQX8W+ffu6jM7q1av7SdwjgAACCCCAAAIIIIAAAggggAACCCCAAAJ5JpDjJvDRNb/22mujJ2X4fPTo0RnOYwYCCCCAAAIIIIAAAggggAACCCCAAAIIIJBTgcADoPFWaMeOHTZ27Nh4F2c5BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAg2wIJN4Hv1auX6+tz1KhR5kd9X716tTVr1izLSuzevdvWrVtnCoJSEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBZAkkFABdv369DR8+3NXpjTfeMAVDVapUqWIaFf6zzz5zz/mDAAIIIIAAAggggAACCCCAAAIIIIAAAgjkpUBCAdCKFSvaPffcY/PmzbNzzz03ov49evSwNWvWWO/evd2ASBEz//8TZYD++uuv9vDDD8eazTQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAQgYQCoNpzv379YlagS5cutmfPHotnMKQtW7bE3AYTEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBIAQCHwSpcuXK1r59+yzrtn37duvevXuWy7EAAggggAACCCCAAAIIIIAAAggggEDBENi5c6dt3bo13x3sH3/8ke1jUheUlGAEEs4AzWz3tWvXzmy2m3frrbfaoEGDslyOBRBAAAEEEEAAAQQQQAABBBBAAAEEsieggafVPWFGrW/Lli1rVatWdbe2bdvagQcemL0dJGHpjRs32pFHHmkKgqrbxWrVqiVhL7mzyb/++stmzpxp77zzjr311lvWtGlTe/755+PeuWJmd955p9144432yCOPxL0eC8YWSCgA+ueff7qTWLRoUdtnn32scOHC7laoUCG3F92HP9bE8OcaLV6DJzVp0sQuueQStw5/EEAAAQQQQACBWAL777+/HX300W5WKnwxj1VHpiGAAAII5D8BjX3hP3804C8FgXQTKF68uD3xxBP29ttvW4cOHVz1Fb958MEHXSxnwYIFLjinIF2xYsXs/PPPd4lqefl6X7Vqla1du9bVddmyZWkdAP39999t1qxZNnr0aFMcTAHQ7BQFgFXmzp2bndVYNgOBhAKg++67r5133nmmyHxOyosvvkgANCeArIsAAggggEABEDj99NOtWbNm7khLly5dAI6YQ0QAAQQQSAWBdu3a2fHHH++qwudPKpwR6pCowJlnnmm1atWyJUuW2KGHHmo33XRTxKZef/11u/DCC23kyJH21Vdf2YwZM6xMmTIRywT9RIHOUqVK7TV4dr169ey1116zbdu22UknnRT0bnN1e7pw36dPH1u6dKk9+eST2d738OHD3Xfgs846K9vrssLeAgn1AaqszzPOOMNtTcFQpU37W/gHQ8mSJUPT/Xzd64qD1pszZ46Lgu9dLaYggAACCCCAAAIIIIAAAggggAACCAQhUL58ebcZxWOiS6dOneyBBx5wk7/99lsbOHBg9CKBPtfA2d26dbM1a9bE3K7q06NHj1BL4pgLpdHERIPJBx10kPXq1ctq1KiRRkebulXd+5UfZ11PO+0069u3r4vKb9q0yfzt8ssvt2uuucYUzVentX56+P38+fPthBNOsK+//tryMrU6zkNlMQQQQAABBBBAAAEEEEAAAQQQQCBtBXy3hBkdQJs2bUKzPvnkk9DjZDxQLOnTTz9NxqbZJgIZCiQcAD3llFPs5JNPdtmcfutKUVbnro8//nimgc26deta165d7aqrrvKrco8AAggggAACCCCAAAIIIIAAAgggkAcCSk7zQdLo7g7//vtvW7hwob377rtuPJisRmjfvHmz6abyzz//2Hfffefu9Xzw4MGuD1I/T/N1iy67du2yDRs2RE92z9WUf9q0aa5pudZVRmmskt16a7AoJe/5olHb1U9qViXe+oRvR3VTcqDiaFkV9R+aUfnll19Cs3bv3u3Oz8qVK0PTePA/gYQDoOrLQKOEhZdvvvnGzj777NA/Tfi86Mf169c3XVXYvn179CyeI4AAAggggAACCCCAAAIIIIAAAgjkkoD6/vSByPDBepSpqcHA7rvvPlu/fr1LetMAYXoeXhTQmz59ul122WV28MEHm+JDH330kR122GGm+E/nzp3dgEz3339/aDX1sattnXrqqW6atvHxxx/bv/71L5dUp8Gbwstvv/1m6pv31ltvtZ9//tkefvhhN4K9Xz982XjrrWPSyOwaJOqAAw5w+1dAU33QayDOo446yg3E9MMPP4Rv3j3OTn38yuvWrXPJgGre3qBBAytXrpz17t3bzw7da9sawKp58+ahvvD9TLWmvv32213T+CuuuMINGnXBBRdYpUqV3LJVq1a11q1bmwZhovxPIKFBkP63euSjatWqmUbpiqfoxbhz505btGiRNWzYMJ5VWAYBBBBAAAEEEEAAAQQQQAABBBBAIGCBF154IbRFP+jO4sWLTd0fHn744aZBrH1RduYdd9zhAoMaOV7l2WeftcceeyyUMakRzF9++WWrWbOmy9TUtjTYksaTqV69ultHo5v7x5owbtw4+/zzz13gzy0Q9adnz54uY3LKlCluzqWXXmpNmjQJZZT6xbNT759++skN+uSDrR9++KE99NBDpn5IO3bs6PpG1SBGasWsjM3wEm99/DrqKlIDUinwKa9JkybZ2LFjbdiwYXbssce6wca1rOJkkydPtkcffdQNXBXeB+iKFSvcPD+yfIkSJVzwVsFkLa+gswK6Oo6WLVua+nTVOD4Us4QzQGPhHXLIIW5gow8++CDW7NA0jSimtGedhCOOOCI0nQcIIIAAAggggAACCCCAAAIIIIAAAskXUManktiURThq1Ci3Qw2ApAGKVN566y37888/9xoR/sgjj3TzNVq7L1deeaWFx4IUoFNG6Pvvv++CmgroRRff5N5PVxajulRUJmN0UT20jfBgoJa5+OKL91o+O/U+7rjj7LnnnnOZqNrejz/+aAqwKivz6quvdkFcTVdT+PCm5dmpj9ZX+f77723ChAk2YsQIFwhVoFIBURUFLH2pXbu23XDDDTEzQw899FAXfFZGrYqCszp3GjH+oosuspEjR5oPZitr9ZlnnvGbLfD3gQZApalIua4WKGVZKc++bwj1paBIvv6xlJ6sF4uuJBQvXrzAnwQAEEAAAQQQQAABBBBAAAEEEEAAgWQL/Prrr9aiRQs3MHXZsmVdhqYCcV26dHEBzDvvvDNUBQXZlAl54403hqbpgY/jRPdNWblyZStVqpRbVsHDYsWKucfKTlTCXHSJDoD6+apXdClatKgLxCpr0meAahmNan/bbbdFLJ7demtlNcVXad++vSmr0hdla5YsWdI9VT+ovmSnPn4dZauGZ7xquu9uQP2kRpdYDn4ZWasoc1Tj7ISXCy+80GV/atr48ePDZxXox4E2gZekotfK7lS0XP0VqOjFEt2xa61atUz/ZBQEEEAAAQQQQAABBBBAAAEEEEAAgeQLKNDXr18/N3BQ6dKlXR+a6jPSByvDa6Bsy/AsTw0KpGbqPpazY8eO8MXdY21TiXDKVMyqZBQAjbWegq5qhq6m40qmu/baa+3uu+922Z96Hl4SqXf4+uGPFehUP52KaSmRz5fs1MevE+te/aWqJNpfZ0aGSkzUuDvqz5TyX4HAM0C1WaXqqm8EBUN1MsKDn2r2rqsHX375pZUvX57zgAACCCCAAAIIIIAAAggggAACCCCQCwLKbtRAQuqLU31Eqo/OWMHP8Kqo78rrr7/elFmoTE61+A2iZBS8y2jbAwYMcIMSqem+msorsc439461TrLq7feV3fr49XLj3meaKrDqB7fKjf2m8j6SEgDVAdepUyfUX4Sawr/yyis2a9YsN2rYI488YhUqVEhlF+qGAAIIIIAAAggggAACCCCAAAIIFGgB9S+pQZCUJapBjDQ6enYDlxkBZnc7GjldcSWNgK7kOmWkXnLJJS4bdM+ePRG7SWa9/Y6yUx+/Tm7d+4GPlA2bXefcqmNu7ydpAVB/IGr+3qhRIzv77LNd3wRKh6YggAACCCCAAALxCrz55pt20kknuVtmV/nj3R7LIYAAAgggEI/AO++8E/r80cAiFAQKmoAG7LnssstcPCe6n80gLLITmPvrr79MTe6VwapuF9VnZtu2bV01nnzySdP3RV+SXW/tJzv18fXKzXv19ariB6zKzX2n6r6SHgDN7MD79++f2WzmIYAAAggggAACtmnTJlu8eLG7bdiwAREEEEAAAQRyRWDz5s2hz5/169fnyj7ZCQKpJPDoo4+65tO6EJ2MEp21mdk+NAL7PffcE1pEWanvvfeea5avieGD/SS73tpfduqj5XO7fP31126XypCl/FcgaQFQvRg++OADlyKtNOnwmzrRHThwYGiQJE4GAggggAACCCCAAAIIIIAAAggggEByBNRcXGX79u1x72DOnDluWWVUhhc/CFL4eC9+vu9vcufOnX5SxL1GL/eZn4ob+fL333/7h26AJj3ZvXt3aJoeaAAmv309L1KkiPXq1UsPQyO163Ei9Q7fv7YRXnw9wvet+fHWx68fK+Drp/llYu031jy/nC7URJd169a5umnU+c6dO0fPLrDPAw+A/vDDD65TWvUPoXRkYUff1Bxeo3UlOspVgT1bHDgCCCCAAAIIIIAAAggggAACCCCQDQEFyX7++We3xtq1a2316tVxrd20aVO33LRp01xT+Pvuu8/at2/vujnUDI0wPnToUHvooYfccmp2/dtvv7nHU6dOdffRfzR6uh8h/t5773Ujlffp08cNpK1lFThVHVV++eUXd+//LF++3B588EH/1N1/+umnppHar7jiitD07NZbQUi/T8W0wsuWLVvMt0D69ttvw2dZvPXxx+Hvwzfip2n/0UFj34xdAU01/49V5s2bZw8//HBolgLdirlp0HF1HeWDzaEFCvCDwAOgGk1swYIFjlT9f1apUmWvW6VKlVykvgC7c+gIIIAAAggggAACCCCAAAIIIIBA0gQUNNOI7UcffXQo81N9Vx5zzDEuYKgBqzMrzz33nHXs2NHFb1599VXX7+bw4cPt/vvvt9atW7sMTQ14fdppp5kGuz7nnHNCm1OWqJ4reBpdhgwZ4rapfnZ79Ohh3bp1s4YNG5q2feaZZ5rPLFVT9n//+98u0Kpt1K5d24YNG2adOnVy9+qfVP3zTpo0yZo3bx7aTXbqrQBqmzZtTIFOFTWlv/XWW10XTO+++661bNnS9fepeRp5Xsl8vmRVHwUwu3fv7lpEa5358+c7qy+//NIUlNaxyU1Fx6yuBj7//HOXwao6+MCmzuNZZ51lo0ePdsuG/9G5VBD1sMMOcwNU1apVywU9Z86cafXq1QtftMA/LhqkgAKfK1assJo1a9rLL79sDRo0cJH4WPvQi0svJAoCCCCAAAIIIIAAAggggAACCCCAQLACyrZU0C7Ron4233jjDZcdqYxCP7K4tqcuD9VPe7ly5dzm69atG2qOntX+zjvvPBcIVBP4OnXqhLIUb7zxRtMtVlHgVgHEYsWKudbEyr7s0qWLHXLIIaH1/XrZqbfW+eijj/yqEfca8V63WOXggw/Osj5qGR3eN2n0dpRRq1usosHElV2bVVHioYLCGqRK8TgFPUuVKpXVagVyfqAB0GrVqrkX4y233GKNGzfOFLRMmTIZnuhMV2QmAggggAACCCCAAAIIIIAAAggggECuCBxwwAEx9+ODnzFnZjGxbNmyplu8Rf196qaiVsW6ZVWSUW+/z0Tq49dNxv2BBx5oulEyFgi0CXzp0qVdGrRPV854t/+dc8IJJ2S1CPMRQAABBBBAAAEEEEAAAQQQQAABBBBAAIGEBQINgKoWjz32mOvfIJ6RxQYPHpxwxVkRAQQQQAABBBBAAAEEEEAAAQQQQACBgibgY267du0qaIee8PEG2gRe/T+oH1D1VaCR3zU6WEZFI2upL4R4+jTIaBtMRwABBBBAAAEEEEAAAQQQQAABBBBAoCAJaJAjlcWLF9vWrVvp9zOOkx9oALRw4cJu9K6dO3e6XU+ZMiWOKrAIAggggAACCCCAAAIIIIAAAggggAACCGQmoIGghg8fbn/88YdLPtSySkBs0qSJ9e/f3/bdd9/MVi/Q8wINgGpgo7Zt29rbb79tFStWtAoVKsTE/eeff9woYhoJnoIAAggggAACCGQmoNEsr7rqKreIvtxREEAAAQQQyA0BjU7tP3+aNm2aG7tkHwgggECmAkcddZQ9++yzmS7DzNgCgQZAtYsuXbq40bhGjhwZe4//f+qePXusTZs2mS7DTAQQQAABBBBAQEHPww47zEFowEUKAggggAACuSGgrt1q1qzpdsXnT26Isw8EEEAgeQKBB0A7dOhgulKWVSlatKgNGjQoq8WYjwACCCCAAAIIIIAAAggggAACCCCAAAIIJCwQeABUTd91U1m7dq3NmTPHvv/+eytUqJCpCZuyOHzT+ObNmydccVZEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSyEgg8AKod7t6924YMGeIyPHft2hVRh1KlSlm/fv2sd+/eVqxYsYh5PEEAAQQQQAABBBBAAAEEEEAAAQQQQAABBIIUKBzkxrQtDXB05pln2l133WXhwc9y5cpZiRIlbOvWrda3b1/X/6f6AaUggAACCCCAAAIIIIAAAggggAACCCCAAALJEgg8A3TEiBE2depUO+CAA+z888+37t27W926dU2ZnyobNmxwo8Q/8MADLhCqewoCCCCAAAIIpK7A/PnzbfLkyfbFF1/YokWL3Ge5WnuUKVPGqlevbg0aNLDWrVtb27Ztad2RuqeRmiGAAAIIpKjA6tWr7Z133rEZM2bYwoULbc2aNbZ9+3b3G7pKlSquK7mWLVvaGWecEepuLkUPhWohgAACKSsQeAB06NCh1qpVK3v55Zdt//333+vA1f/nRRddZJ06dbKTTz7Ztm3bZiVLltxrOSYggAACCCCAQN4J/PXXXzZ+/Hh78MEHbd68eTEr8vvvv9uSJUts2rRpNmzYMCtfvrxddtll1qdPH6tcuXLMdZiIAAIIIIAAAv8V+Oyzz2zw4MHuIqM+d6PLunXr7Oeff3YXIJ977jnbZ599rHPnzi6R6Oijj45enOcIIIAAApkIBNoE/s8//7SNGzfalClTYgY/w+uhrJEePXrY3LlzwyfzGAEEEEAAAQTyWODrr7+2pk2b2oUXXphh8DNWFfUdQAHTww8/3IYPH+66xYm1HNMQQAABBBAoyAIaLLhbt26mQYGV+Rkr+BnLR60vJk6caI0bN7Yrr7zStmzZEmsxpiGAAAIIxBAINAC6YsUK1wQu3sGNNm/e7K5oxagXkxBAAAEEEEAgDwSefPJJO/HEE23OnDkx967P+EMPPdRq1qxp6t87Vvnjjz+sV69erqkeP85iCTENAQQQQKCgCnz++eem7E0FMmOVwoULm5q916pVy7WmKFSo0F6LadyNZ555xpo0aWLffffdXvOZgAACCCCwt0CgAVC9SS9evHjvvcSYosGQ1Ey+fv36MeYyCQEEEEAAAQRyW2DAgAF27bXXRgxiqDoo2Hnfffe5bFB1XaPmeGr6roxP9Vs2btw4O/300/eq7nvvvee6xVH/3xQEEEAAAQQKuoDGylCf2frsDC9qHXnDDTfYRx99ZGpVuWrVKvvpp59cX6C6kKgWlldccYUVL148fDW3TIsWLWz27NkR03mCAAIIILC3QKAB0H333dd0herVV1/de09hU/SjqVmzZu4NXQMkURBAAAEEEEAgbwWeeOIJ69+/f0Ql1G/3o48+aj/88IPrb0yDHRUpUiRimQMPPNANeKgmfMpqOeGEEyLmK5O0Q4cOewVVIxbK4smuXbtcsFUB1x07dmSxNLMRQAABBBAIRiDIzx91L3P22We7wY187ZTtqcCnfh+r65iTTjrJSpQo4We7+/32289OPfVU02DDSja6+OKLI+brs1EXIbUNCgIIIIBAxgKBBkC1G43q3rVrVzvvvPNcRohGjNUb9ccff2zquLldu3ZWu3Ztl0WiH1vRP6QyripzEEAAAQQQQCAZAsocUZP18KILlJp+/fXXu0EXwudl9Pj44493I9hqnfAyc+ZMu+WWW8InZevxhAkT3Ai49erVMzXRpyCAAAIIIJAbApMmTQp9/jz22GMJ71JZnPqNrFaQvijr8+2333aBz1iDB/vlwu+rVq1qzz//vI0ZMyYiG1SDJWn7CthSEEAAAQRiCwQ+CnybNm2sX79+NnDgQHvppZdi7/U/U/VDq3v37hnOZwYCCCCAAAIIJF9gz549dvnll5sGVvBFgUYFLfXjLLtFFzaVNaoR4fVdwBf9cNTnvoKkFAQQQAABBAqSgH4fL1u2LHTIpUqVcs3dGzVqFJqWnQfnn3++qQVG+/btTZ/jKmpxoYEI//3vf2dnUyyLAAIIFBiBwDNAJac+xKZPnx6zf0/9qPrggw9s2LBhBQaZA0UAAQQQQCBVBV588UX79ttvQ9WrWLGivfXWWwkFP0Mb+c8DNac/55xzQpM0YMOtt94aes4DBBBAAAEECoLA0qVLTS0fw8vo0aMt0eCn3476ElWz+fAyePBgo9/tcBEeI4AAAv8TSEoAVJtv1aqVzZ8/39avX++av3/66ae2adMmW7Bggev4+X9V4BECCCCAAAII5JWAskXCy6BBg6xGjRrhkxJ6rD7B9YMvPIt0xowZNmvWrIS2x0oIIIAAAgiko4ASf/76669Q1XVxUH2BBlE0cGHz5s1Dm9IASk899VToOQ8QQAABBP4nkLQAqN+FBlBo2bKlG/SobNmyfjL3CCCAAAIIIJDHAl999ZV9//33oVocfvjhrjl8aEIOH1SqVMn69OkTsRVlvVAQQAABBBAoCAJqnj5+/PjQoRYtWtTuu+++0PMgHtx///0Rm+FzNoKDJwgggEBIIOE+QDXaqzpz3rZtm+vMWR066/Hdd99temPPqCjzQz+2evbsmdEiTEcAAQQQQACBXBB47733IvZy9dVXZ/oZHrFwnE+0TfUF6vsoi95nnJthMQQQQAABBNJO4Msvv3QtIn3FNZq7LjYGWU488UTXnF59gKr8+OOPrr/RIFpzBFlPtoUAAgjktUDGkcosaqYR8TQCnS8NGza0O+64I8sfTk2aNHEdM1euXNlOP/10vzr3CCCAAAIIIPD/BUaMGBFYU3E1u9u5c6cbLbZw4ciGH1OnTo0w18BH3333XcS0IJ6oNcjatWvdpjQIRI8ePaxEiRJxb/qHH34ILfvaa6/Z8uXLQ88ze/D0009n+b0ks/WZhwACCCCQXgJLliwJNMNSwURf3nzzTfv111/907ju1SVceNm4caNddtll4ZMCeax+tsOLko1q1aoVmvT333/bjh07rFixYnnyuags1XhHug9VmgcIIIBAwAIJB0Bvu+02FwDVCHZjx461jh07mvr7yqooO3TMmDGuSfwnn3xiVatWzWoV5iOAAAIIIFCgBDSQ4IQJE3L9mF955ZVc2Wd4c8Ds7lAZLj7LJat1H3/88Tz5oZdVvZiPAAIIIJAcgd9++81GjhyZlI3PnTvXdMtJ+fzzz023ZJePP/7YjcOR7P3Eu/1+/foRAI0Xi+UQQCBpAgkHQP2b/6hRo+yss87KVgWrVKnirnypQ+iHHnooW+uyMAIIIIAAAggggAACCCCAAAIIIIBAMAI//fSTrVmzJpiNZbIVJcQdf/zxmSzBLASSJ5BwAFTN5C644ALr2rVrQrVT6v8RRxzh+gVTFikFAQQQQAABBBBAAAEEEEAAAQQQQCB3BYYOHWrPPPNM0ndarlw5U1cQFATyQiCyM7Bs1GDRokV28sknZ2ONyEUPPPBA1//XwoULI2fwDAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQCEgg4QDo4sWL7ZhjjslRNQ466CBTqjUFAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIhkDCTeB3795tf/zxR8J10kh1S5cute3btye8DVZEAAEEEEAgPwo8++yzpgF8gii7du2yDRs2WMWKFW2fffaJ2GSvXr3sxRdfDE0bN26ctWvXLvQ8iAcaebZu3br2+++/u80VL17cjeIeXZfM9rVt2zbbsmWLW2S//fYz3eIp2hcFAQQQQKDgCBx77LG2fv36wA440c8fX4HXXnvNLr/8cv/Urr76arv33ntDz4N6oO7lXn/99dDm3nrrLTvxxBNDz//66y/3OVy2bFnXCjM0I5ceqNkzBQEEEMhrgYQDoA0aNLAvvvjCjeaeyEEo83Pz5s1Wv379RFZnHQQQQAABBPKtgPrGDqp/bAVAddGxQoUKewVATz/99IgA6Jtvvmndu3cP1HXq1Kmh4Kc23KJFC6tcuXK29qFAZpEiRdw6pUuXjjsAmq2dsDACCCCAQNoLaIAVfd4FVXL6+XPmmWdaoUKF3Oew6qTP2UcffXSvz+Oc1FcXCN9///3QJkqWLGlt27a1fffdNzRNAdA9e/aYApElSpQITecBAgggUJAEEm4Cf/TRR9sbb7xhyuxIpIwZM8YKFy5s9erVS2R11kEAAQQQQACBHAroh5l+KPkyceJE++677/zTQO779+8fsZ1zzz034jlPEEAAAQQQyK8CuuB30kknhQ5v1apVNmLEiNDzIB5o8JqtW7eGNnXWWWdFBD9DM3iAAAIIFHCBHAVAZ8yYYXfccUe2CadPn+5S/2vVqhXxwyvbG2IFBBBAAAEEEEhYQE3JL7jggtD6uqh58803hzJVQjMSfDBhwgT7/PPPQ2uXL1/ezjvvvNBzHiCAAAIIIJDfBa655pqIQxw4cGBgzfSXL19uw4YNi9h+9P4iZvIEgQIgoG4wfvnll71umq5WUZSCK5BwALRjx47WsGFDGzJkiN13331x9+X57rvvuuZ1SsP/17/+VXDlOfJ8KaA+bbObFa3lV6xYEdNDTVfpJzcmDRMRQCAggdtvvz0iU2TKlCk2YMCAHG99wYIFduWVV0ZsR8HVePvvjFiRJwgggAACCKSpwDnnnBPR6nHt2rXWtWtX1yQ9J4ekrM9OnTqZ+in15ZRTTnFdzfjn3CNQEAXU3aK6mjj00EPdrXHjxqYLA+r6SQNxV61a1SUAvPrqq6axbSgFRyDhPkCLFStmasaukeCVBfp///d/ph9Rak53yCGHRPRrsnHjRps/f74NGjQo1D9J69at7YYbbig40gX0SNetW2cjR460HTt2xBRQ00u9Xvybk96Q1E9OOpU1a9bYrFmzXJ+4X375peuAXH3UZVb0hUXLzpw5061XpkwZGzt2bMQqGmSsR7sDAAkAAEAASURBVI8e7k35+eeftwMPPDBiPk8QQACBIARq1Khhffv2tfCm6spOUR9qiX5Oqxn9GWecETFYYu3ata1Pnz5BVJltIIAAAgggkDYC6vbtiSeesFatWoWyz9QiUi0w9B0/kQH7NJaGgqjz5s0LOej3+WOPPRZ6zgMECqrAcccdZ7qpy8bFixfbnXfeGfGdVrEpTevSpYtpbJtRo0aZgqSU/wooQUsXavJj/CHhDFDRaACj+++/3ymtXr3abrzxRlOzdnWsXK1aNRcc3X///d2PKPV94jtnVufLerNPt0DXf18O/M2OgM7/Lbfc4rKFdf51mzZtmumHsDKI9YGvQKDegNQs8tZbbzUFzNOpzJkzxz7++GP79NNPTRmb8byuFTRV+v3ChQtt06ZNMQ9XwWPNU7BU/18UBBBAIFkCupDZrFmz0Ob1/qTP9CuuuCIiiBlaIJMHL7/8sp1wwgkRme0aiGH8+PERmaaZbIJZCCCAAAII5CuBli1bumSh8IN66aWXXP+gS5cuDZ+c5WMFb44//vjQb2u/woMPPmh169b1T7lHoMAL+AHRolsfHXXUUS5p6eGHH7Zvv/3WXZwIug/8dMa/5557XKJWOh9DRnXPUQBUG9UPJI1mpww+X9S8XU16v/766736N1Hmp7LllHZMKRgCCgiqM26NyqiibCMFOzXtuuuus4ceesieeuopq1SpksuGvPjiiyN+OKe60mmnneaCBL6e8fQrogsFaqKiriRUYq0jp3vvvdfuuusu06BjFAQQQCBZAnp/fu211+zwww+P2MWzzz7rLmyqGZGy0jMqeg/76KOPTD/wNMhR+LLKfFGLEa6sZ6THdAQQQACBgiCg1pDK2gwvSgSpU6eO+02tPgszK8pku/TSS93vgh9++CFiUXUtd/3110dM4wkCGQn07NnTGjVqFHFTc/DcKPqOGL1vPVfyT9Alq8Skm266yTp06OC+t6rlJcXsrbfeMrUEy68l4Sbw4SB60SiYIyilGavPBQVBfVGWn65Gqelb9+7d/WTuC5iArrwoo9EHQsMP/7DDDnMf2gr2KQP0gw8+cB/w4cuk8uNSpUqFqpfVG21owf888OtltI6CCRQEEEAgNwR0EUpBzPbt27tua/w+f//9d/fDTN3ctGjRwgUyDz74YPdevmHDBpfJrvV+/fVXv0roXpmfL774oqn/MwoCCCCAAAIFWUAXBNXllZqqh3d9pRZkutCo5uvqXk6tKKpXr+4GC/7zzz9tyZIl9tlnn7lMtVh+vXr1MmWyURCIV0DB9Llz58a7eKDLKU4Ua9951RenWjQr6KdMUP2fhbeImjp1qotv6X+wXr16bqBQddnni+IWyuRWd3a6uPHMM8+4lqEK6KoVVcWKFd3340mTJtknn3ziAr+33Xabew/w29C9srr1nqCLIEWKFLFjjz3Wra/v0eFFdiNGjHDxNrUoVYtaNeNXcpXKnj17TH35v/76664uTz/9tHusrq58XEHHqvlqta3j6ty5c6jPfrXiOv/8892YJk8++aRNnjzZJa4p1qd9azwfdbuhILae6zi1fDqVQAKgOmAFcvTi0W3nzp3uB9GqVatcNolOiN7wKQVbIKMgn1cJz3LUqMG6wklBAAEEEMg9AX2pU9/E1157rQtchu9ZA7Lpi6Bu8RR99msUeP2YoyCAAAIIIICAuXEy1CqiSZMmrv9t/W72Ra0pZs+e7W5+Wmb3Si7ROBxqPUdBAIHEBNQ1nxL2NGbJjBkzQgFQddGn/0k1B1ewUQHO5557zhSnUNa2usDr3bu3ffPNNy7JT8FCbUutONW1lL4DK0FQQVEFEPfZZx/XslOJBbrg4YsCmhpUXIFU9VuqQKxamKqFrFpa16xZ0y2qYGu3bt3swgsvdC1ofQBS+1Cd9H6giyFap3Llym78nQ8//NDU37BaeCkAqrpoMKhly5a5cVgUiFYQUwlqir0o+Hv11Ve7izFKXlC9DzjgALd/Oaj7PtVTRdtS8mO6BUCTEpVUpFrBLI2yJWyCn+41wp8sBPRP7Iv6vcyq6ArH8uXLI0Y+jLXOli1bIkZS/+2330x9fERfZdL+tV9/C/9Cosd+uu7D58Xap6ZpGV1dy2k6v+qpY8isqE66cvTzzz9nOqKkOjSWmd4ktXxWI8zrzT68aN3wpq3h83iMAAL5Q0BfoEaPHu2u+urKcnZL2bJlrX///qZR4IMKfuqKuL5Y6qar1RQEEEAAAQRyQ0CtG/znj1o5BlXU9FbfxdUlWFZJItH7VIaYBlBSM3iCn9E6PEcgewL6f/J95/rWTBqzRJmS6rZC/Yhq/r///W/T4GN+gFCNceMDmfpu+vjjj1u/fv1syJAhdtFFF7lMSWWUajv6f1dAVMHRiRMnhiqo9wB1X6FWsAp+qmhQJvWbr3iFgpK+qzyNlaLBoxUAVVG91Z2g4gC6cKJtK7NTiYkKsh555JGmAKi2owCrioKXitX5LFbF7MqXLx/qT1jTlbWqotZeiuXpe72K6h/e7eXll19uVapUcfPS6U9gGaDpdNDUNTUFdPXEF109yagos/iBBx5wgThdCdEIZerIWF0s+Cskai6iQYn0T6++dfSGpDc0/eP6Ly/659c6au6pojc09YH3wgsvuDcaTdfVGxW9Ob333nuhzKczzzzTlL4eqyiQqisi2peatKioGYsCAj49PdZ64dMUqFR6ua7YqDsAvdH6eoYvp6s2Sm3Xm5UGF1MzVF1wUPbWySefHL6ou5qk/lbV3UDTpk3dMakP30suucS9SfuF9SapK1q6lSxZ0o2Kp2N55ZVX3NUvvdkq1Z5+hrwY9wjkT4F27dqZbhq8Ts3Y1aRG77exiprzaUAGXZnWlWD/ZSnWsolM03uhrlir6Ep3IoHZRPbLOggggAACBVtAzVbVlFUl6H75FVxQwOL7779337eVubVo0aKY4AqSqgmumqvqu7v/zRNzYSYigEBCAmrKrqJsR/3mVdDRF5/U9MUXX7hYgf4nNeCzir6Xli5d2i/qgph60rx589A0La+gpC6oKKip5xrvQ0ldvnm6X1jPFXTV73G1zFIWprJP33nnHb+Iu9d7k7qn0nZVtE0FSVeuXGlnn322m+aDu3qiZu3K/tSxqaxfv95lf+o9KLpoW+FF8RbFVBSn0X41X03r060QAE23M5ZP66ugm9K8VXTV4sorr4x5pEoJ1xWMAQMG2BFHHOGW0ZcFBUT1jzhy5EirVq2auwqiNwtlOqo8//zzLhipQYeU3q50bb0x6E1H/7z6ka+rO5dddpkbvEsBz/CiLCbddHUnq+yjRx55xAVZFSRUtqneJJU5qR/vTzzxhAtAhm871mMFHGSigGxGRVeP1J+Ijl3HrKI0daWt33333c5R/YeoKHtKwV4NPqZ0fl+UXapg7YEHHminnnqqM9IboAKpGnleV3l05UtvkgqqKqisgKz2Xb9+/b2CrH67/l5f6nwQ2E/z98okVaB327ZtflKO73U8+kAJcps5rlQ2NxCeCa3XanSmcjY3l2eL+/Ouc5GurQD0hcQXHY+/Auunpcu9P45EX09qDqObjl/vZcps18UnvTb1RVFftPTFS30J+RL0/2D4/4UeB719X+9k3+s9T0UtBNL99aTj0GsrL86FXgM58dN54PNHZzCyhP+fJfp+EbnFvHmWHz5/wj/788P7RTq/nvxnqF7Nyfr8qf6fRAn9ttFNvx0UBNW9WmopGUFZVvqc9SNaqy7Zfe/l80dqOS85/fzJeQ2C2YJ++4Vn82mrCoYpmzDZxQfpovfjg3LR05P9XJ8ZarWk4rMw58yZ4xKE9Ps3vOgiSDxF/7exir4r6/uL9qlkLCU8qUSfC03T4OH6Xa666fu21lNWZniRpZrjhxc1tdct1u8vXXTRTQliygZV//8qsVq3atvh5eabb3ZJEYrTKBirYKi68ki3QgA03c5YPqiv/uGVhakBkRRkU1BTQTV9wdAowcpK9GnZ4YerL4MKWKo/Ch/81HwFNceNG+cCmgoGqi8cZT8qpVtZQyrKRlJKui8auEv70RcM9buh5ZXBpKL08egAqF9P87IKgOoLigKQvqh5irIl9SVGbxTDhg3zszK895lXqr+cosuPP/7ojlNflHzwU8voyrCyTFVHZYf6AKjS77V/P+iS355fV1e3FQCVgfo30RumArkKEsvJb0dOyq5VcFlvnNFZpn67/l7nK6Pm+zo/+hKhzNugSzK2GXQd49merNO95JcuE7L7QyMVz1sQryf9+PJfDsOPUe/ruiWrhG9bnxXp/j+u92Pd0r3oczkvzoVeD/7HfCKGPgCajLonY5uJHGNO1wni/SKndcjp+vnl8yc/vF+k8+spPBidG58/6otQTWBjlSDeX/LD60k26fr5E+u85sU0JdFEFwW2lBiT7KLf5eq2LVWKAoz+e6a+4yoIrN+vavWkixOJlFjBx1jb8b+T9Xvf97Xpl1OCgYr+75V4oKLf5hm9P7gFsvijY1MTegVfR40a5fo+VYJWdLBTm4meppZeii/06NHDJZkpOUKxDSWQpVNJSh+g6QRAXXNfQE3RFbjTFZShQ4e6NHBlS2rUseHDh2fYpEPZhGr+rjcDn4np7xWYVNEbmP+iov4sfFFHwuFFfdypvw0VvbkpCBtUUV8+4UUB0e7du7tJX331la1YsSJ8dqaPowOWfmGN9KgPj1atWvlJoXs1tb/qqqsiRl1WGr3S4xXcDC8+6KsrfuHFp//rS5gPfvr5yvxUSaUPLl837hFAAAEEEEAAAQQQQAABBBDISkBZlfpdraLfz8qM1e9vZUZqQKRYF6uVFJFRVxVZ7S96vm+eruSm6KJgp4qW8bEOtXyNVeKNL6hZvIKYSh7T7/zMSnQAVDEcBYTVIlSBcgV5FTRXfCadChmg6XS28kld1WfNs88+a/pHV7NwBSyVgeg79M3oMJVxqH809cUZXRTIU/NvFV1J8Z33+uWi/4E1XdmmSkPXG1uQ/7g+qOj3rXtlV+oqi4reoGKlubuZcfzRFSClwyvbM9Zx6Y1Jt/CijFrf+bGm641bzezfffddt5i/6hW+TkaPfd9+8WTEKXs0o23rfCrdPry/lIz2Ge90bU+vJwW407Xo9eibW6n5RF41B8mpn86DmrzpXMR6neZ0+7mxvl5P/vWrLwlqTpKOxTfZS+fXU7i9/ieCfN/IzXOqL9rKhErn15Pen/wPAp2XrL5AJ8NX+83J+4q+S+gW5OvIv18Euc1k2GW2TT5/MtPJ3Xn+9aS9pvP7RX77/NFIyen6P54fPn/860n/FzoX4V3vaFpuFH3+6PODkh4CWbUWuf32223s2LGur87HHnssdFBt2rRxAxENHjzY9T3vZ2h7WkddxGVWstqvX1cBSbWEVUBSyUrhRV35KStUiUwbNmxwv0PU3Z/62g/vW1Rd16l/0qziC4p3TJ061bXk8t+h9DtHWaHhTfb9d+7o3/pKXtP+9frXAEhKNlPLXI25okHZ0qXkSQBULwg1MWYQg3R5mSSnnmrG3qtXL9eHpZrCq29KXYHRB1qsooxDvXbUl2VQXz7Up87SpUtDaeWx9hvENPW96YOtGQ0iEu9+1AefSiIf+npz1GAmqoO6DtCbbqxOj90OAviT2ZuhrpxphLogg5X6cqcf50FuMwCGbG1CgcPwAKj/EMrWRlJgYX1oKgCqq6jp+kVR7zfhAdBE/udS4FS4Y9D50JebdH09hX8u6HG6/o/rx5sPgKbz68kHQPPqXGi/OXlf0bq6Bfk60udPul+A4/MnFd6x/1sHvZ74/EmN8xH++aMLcEG+b+TmEeaXzx8flNH3mbw4F3o9+OBRbp4/9pWYgPrSVfEDGOmxvt8r+UpNv9UCVcHOMWPGRHxHVlxCAwBrMCT9ZtVvZgUQtbwGSfatJRXDUInunkIBS5XoFpO+HkpoUrapsk5Hjx7tbhrHw8fH1DpTmZpKGtO+dNOATOperm3btq7ZuQYjevvtt91FsvDMUP2+Vn20Lz9Ik+qiJCbdZs2a5QKZGtRIx633NR2HYgL6v/LZphoEWfvSIKgKdE6bNs0WLlwY6nNU9df/gwZXTqcSO9KUgyMQkjLLMiv60qkgjD7Y0w0ss+NiXvYFFIRTs3VldaqzYV15uemmm2JuyPcfpAxKZT8GUfyXmlh9jgax/fBt6A1IgxFVrlw5fHK2H/s3VG0rO0Wjxqn/0Z49e4aMdRWIggAC/xPQVVR94VBRAFdZOCoKVsXK7nYzE/ij957rrrsugTVZBQEEEEAgNwX0fUs/hHOj6Ed2Vlk8uVEP9oEAAgiks4BaOT7/n0GQfQBSAc0XXnjBDSSk5AwF+ZSMpexLDTYUXTSwkOZ169bNDRakAYPUD77GyDj99NPd4hpjY+DAge6xltU4JGoSrqClRltXUfd+Cl7qvV2/wxV8VNFvAI1PojExFDvTeCFnnHGGm66BjpRpqeBkePd1CsYqgPn/2LsPcCeK9+3jD02KUix0RUGwoggiIqJgQaoUQUVFRBTBzg9sWFAUEQsWQMXeABsK2LFhL1QbYENRUbCgIAJShNd73v/ETU5OzSYnOfnOdYUkW2ZnPxtOkicz8+iYmntT83j2798/kl9EwckxY8bY8uXL3TE0ulYB1u7du7vnOm+NCJWFjqcpAm+66SaXWOnSSy91vTqV6Fi5SOSjYK86LfrgqmIZqktzfmq0rWI42j6Ym8UdKM3/CTUAql8r586dW6BTVuCrR48eLiDErygFIiuxGymjmLIKKzHS008/7TKTdenSJcf5qhel/mPrNZZXAFTzZcRmSMtR2f8t8L0xU/Fh00/GrykAEilyUNEvWjJr2LBh3OrUQ8f3MNIf5dGjR7uh+Oo2T0EAgfgC+pKrDzDJLhrSSAA02crUjwACCCQuoCmbcvtxPvHao2vQXG+p+EwafVSeIYAAAiVLQEFKH6gs6plpSLp6fer7tkZIqMdlcGoyJQHS8O/YoszxsdnjtY0Cj7rFFvVk1lR5SoSkThgaWq6gqO+o5bdXzOzCCy80xU40glV5URQE9UVBWwVfdcutqF0Kmipu5+ME6lmq7yTBYfCKzajTmabV87E6DclXD1HFWvQ9JtOSH3mThCewUPdafTDwN3Up1hBdRduFpl9NNWGqoPQC0jLhKfmNlimgRcluAf3H1TwafhiDfrmIl4Vdf3RUHn30UfPBxFg5BS9mz54duzjucwUQ9YdGWdN93cEN/RC/4LKiPlY3dx1LH2zVXTyRog/G/o+vJiDWH7DYol6iwT9+PtOffmWiIIAAAggggAACCCCAAAIIIIBA3gLqbKTgov/+nffWRV9brVo1a9u2rRsGHxv8DNaq0dRqUzD4GVyf32MFL33w028bDH5qmc5VvUB98FPLNBJOz9UZKzi0XusyqSQcANWkqZoEVZmu9cJQQFRYihbvvPPO7ldMRacFpV55WtaqVSvTcFzBFrSnXiah0tb4An7OFg0rjS16HagbuIrmP9RjzV8RLL1793ZzdmkovH6pCK7XPpqfQpni4/3a44fPB+vzQcEBAwZE/efWZMMq+uVD8+X4ou7kfr5MP3+HX5ff/bPPPuvarl9sgn9IfP3xgpiq06/3c0Jqmf5gqTu+ynvvveeCx36YrpbpR4YRI0ZEMs9rmc5FRXN3BItPKBV7TXJrj/YNtiVYF48RQAABBBBAAAEEEEAAAQQQQACBdBRIOACquRNnzpzpEtMU9AQVAFIgVPMa+IzSBd2X7TJTQL19fZBOgcR4PTjVjdwnzVEPRnXx1gTAviiIfsIJJ7inmnNCQ7kVULzyyivt6KOPtrFjx5oytcWbp09ziwYnJ9ZrVvNaaEJfHTdY9t9/f/dUPZY1H4aC9eqxrDkzDjroILdO85VqmKwfQq9fTfRrjErsPFEffPCBaRJhtTW2p6nOQ0W9Q32w0y349x/Nkaueoyr+OO7Jv/9oHk/90KCieTw1h4cmTlaXdt00hUDwlxn9QKGi6QNkpMmW1Z5GjRq55bo+yoCndqr44LJ+4IgN9vrJntW7219TtxP/IIAAAggggAACCCCAAAIIIIAAAmkoEMocoOqie+ONN7qJYa+//vpIAol456vgp5LAqOstpeQLKHh23333mSYJ9kVBMwXwDjzwQHcfDNSpN7Gf53Px4sXWr18/NzGxssWrKMin7uGaGFhD1GfNmuWWq1exssNXr17dPY/9R71CzzvvPLfvn3/+6QKLmhTYB1SD2zdu3Nhlp9fkxephqV6fhx56qMu6puCmejRrsuTWrVtHjqdzUAZ7Dc/XhMvqiaqh6gokqsemgrPBuTo1ZYSCjcpAp6KAsIKthx9+uJt4WMFZHdsHGNVbVXOPKLCp3rL6/6Ph7wrCapJnBUsXLFjgfljQBMvK6hYsl1xyiZssWdNP6JgtW7a0888/352LMtspMPrmm2+atvN1+v010bJ6nCowrPNQT1sVHVNZ8NSDNtMmP/bnxj0CCGSGgP4G6e+wiu+lnxktp5UIIIAAApksoM4S+p6hwvtPJl9J2o4AAgiYhRIA9ZDDhg1zPTo1/J2CgAQUGLz44osLjKFpEZRdLa+iofDHHXecffvtty5wqDk8K1eunNcubi4N9S7V8HBN3pvfBPM9e/Z0vSrVC1Q9Lf2cH/oQpKBsvKIAYbD3qIKXwfk6g/soYKieq7kVZXwLZn2Lt53m/VAvTgUyNeeuMtNp0uR4RUFbJUFSj1JZBecVkbcCsN5QkyDnlpxFUw/oRkEAAQRSKaC/d/7vGz+gplKeYyGAAALZLaCRZbz/ZPdrIFvO/uyzz3adbZJ9vvouTkGguARCDYDqJOJlvMrt5K666irTjYJAYQU03FyBz8IU/bEtzD4a1h7stalj+URN+R1XU0OkqiiYWdDz8h/gYtvmg5+xy3mOAAIIIIAAAggggAACCCBQsgWaNGniOg2V7LPk7LJdIPQAqAf1Gd7jJZ9RghVl+b7jjjsIgHow7hFAAAEEEEAAAQQQQAABBBBAAAEEEEAgdIHQA6Cff/65G7rrk7uE3mIqRKCAAn7+TG2u+TMpCCCAAAIIIIAAAggggAACCCCAAALZJxB6ALR9+/ZunkVRaghxvCzvmzZtMmX5js16nX38nHEyBZQUyJevv/7aJV3yz7lHAIHMFdi8ebNpJEEyiupORVH7k/ke6OctTsW5cAwEEEAgWQJF+TupffwtjPeKorShqB6+3f5e9QQfF7Xewu7He0hhxdgeAQQQQCATBEINgKrXp5LMNGjQwJTFet99941KthIEUSZun9E1uJzHCIQh8Pjjj9s777xjjRo1ctW9/vrrtnTpUjvssMOsRYsWYRyCOhBAoJgE9N7x7rvvFtPRwzmseqgHk5GFU+t/tSxatMj22GOP/xbwCAEEEMgwgaefftqUlDKbSqdOndLidPU9Tok/KQgggAACCJQkgVADoDvvvLMpU96FF15ozZo1y9OpSpUqNmrUqDy3YSUCRRU4/vjjTTcKAggggAACCCCAAAIIIIAAAggggEB2C5QO8/SVSfqII46wtWvXFqjagw46qEDbsRECCCCAAAIIIIAAAggggAACCCCAAAIIIFAUgVADoGrAuHHjbNq0abZu3bp823Pdddfluw0bIIAAAggggAACCCCAAAIIIIAAAggggAACRRUIdQj8ypUrTfOANm3a1Hr06GEdOnTItV3KFv/oo4/aDTfckOs2rEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBIRCDUAWrp0aTfvopI7qMyYMSORtrEvAggggAACOQTatm1rNWvWzLE8jAUfffSRffPNN2FUlWcder/s3r17ntskslJT0pSkMnfuXJs8ebI7paOPPtrSJVFISTLmXBBIN4G6devaMcccU+hmbdq0yTZu3GgVKlSwUqVKFXr/2B1++eUXl1gzdnkynh988MHu/U3t13moKL9CqrOy77jjjsk4vYysc/78+TZx4kTX9s6dO1uXLl0y8jxoNAIIIICAWagBUCU2ateunT333HO2/fbb23bbbRfXeMuWLaYPE8oET0EAAQQQQKAwAiNHjizM5oXa9txzz7Xx48cXap+ibKwvtE899VRRds3KfRYsWGATJkxw516nTh0CoFn5KuCks03gwAMPLNLfSeUiWLVqlQsk6semRMurr77qvt8kWk9B9h8+fLgdddRRtnr1avvrr7/cLtWqVbOKFSsWZHe2SYLAokWLIu8/NWrUIACaBGOqRAABBFIlEGoAVI3u2bOnVa9e3e6///48z0G/ah555JF5bsNKBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgEYHQA6Aamrbnnnvm26ayZctaMnvx5NsANkAAAQQQQAABBBBAAAEEEEAAAQQQQACBEi8QegBUQ991i1eUGV5z2Gjon0rr1q3jbcYyBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgFIHEJ8bJpRkzZ840zWMTnEvtjz/+cPN2PfTQQ7nsxWIEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCA8gdADoJrbs3///nb44YfbNddcE5U1UYkLnnjiCXvggQds8ODB4Z0FNSGAAAIIIIAAAggggAACCCCAAAIIIIAAAnEEQh8CP2bMGBfgjHMst0iZ4R955BGrV6+e7bffftavX7/cNmU5AggggAACCCCAAAIIIIAAAggggECaCfz9999uesPSpUPvV5dmZ0pzSopAqK/ULVu22KhRo2yfffaxiRMn2ksvvRTXaaeddrJmzZrZ0KFD7Z9//om7DQsRQAABBBBAAAEEEEAAAQQQQAABBIpXYMmSJabObkp6Xb9+fVNS64oVK7r76tWru/wuF198sb311lumuBAFgXQUCLUH6DfffGMbNmwwzf+pREiffvpprufcuHFjmzdvnn311Ve2xx575LodKxBAAAEEEEAAAQQQQAABBBBAAAEEUivwzjvvuKkNX3nllbiBTQU7f/vtN3d799137YYbbrBdd93VLrzwQjvttNNcgDS1LeZoCOQuEGoP0MqVK9vee++daxb4YDNmz57tni5btiy4mMcIIIAAAggggAACCCCAAAIIIIAAAsUksHLlSuvTp48dcsgh9vLLL8cNfubWtMWLF9ugQYPclIc+7pPbtixHIJUCofYArVGjhikJkoa1lylTJtfzmDBhgi1atMitb9KkSa7bsQIBBBBAAIFUClx77bU2bNgwd8jVq1fb2rVr3eMqVaq4YT5htaVUqVJhVZUV9VSrVs0aNWrkzlVziVMQQACBVAkceuih9uOPP6bkcBpBR0kvgapVq0bef7g+6XVtaE3yBD777DPr0qWLfffdd3EPoo5vDRs2NP3/0Dyg33//vf300085tl2wYIEdfPDBNm7cOBs4cGCO9SxAINUCoQZA1fi+ffvaGWecYXfddVeOc9GXSc0bMXr0aLeuRYsWxheZHEwsQAABBBAoJgEFOnVT+fPPP23NmjXusQJwmueIUjwCXbt2tTZt2riD60M3BQEEEEiVwFZbbWV16tRJ1eE4TpoJdO7c2c1tqGbx/pNmF4fmJEVg7ty5duSRR5p6gAaLPh+rV2fv3r1dz87YH/MVAJ0+fbqNHz/eFi5cGNl148aNbj/VpzlCKQgUp0CoQ+B1IkOGDLF169ZZ7dq17fLLL7fPP//cBg8ebB06dLBddtnFRowYYevXr3frp0yZUpznzrERQAABBBBAAAEEEEAAAQQQQACBrBdQj8+OHTvmCH6eddZZ9vXXX9v1119vTZs2tdjgp+D0Q9GZZ55pn3zyid177705Orpdcskl9vDDD2e9MQDFKxB6AFSnowzw+s8xZ84c+/jjj+22226zGTNm2O+//+6GxquXqCbIVTZ4CgIIIIAAAggggAACCCCAAAIIIIBA8QhoGkP17vz1118jDdDop8cee8xuv/12U6b3ghRNhajkR7NmzbK99torahf1IPVTIUat4AkCKRIIfQi82l26dGnr37+/nXjiiS4TvHqBqsuzsoFpzs+6deum6PQ4DAIIIIAAAggggAACCCCAAAIIIIBAbgJ33HGHffDBB5HVCmROmzbNjjrqqMiywjxQ7Oftt9+2Aw880PUe1b4aKawg6JtvvlmYqtgWgdAEkhIA9a2rUKGCHXDAAe7mlwXvn3/+edO8KhQEEEAAAQQQQAABBBBAAAEEEEAAgdQKKOnn1VdfHXXQG2+8scjBT1+R8r0888wzptwvf/31l1v81ltv2QsvvGCdOnXym4V+/8svv7hpF2Mr1jymStyUrkXX4ZVXXnGdBjV9ZF5Fycc1qlqjrocOHRrZ9KuvvrKXXnrJBgwYYIrHUaIFkjIEPvoQ8Z9t2LDBTZAbfy1LEUAAAQQQQAABBBBAAAEEEEAAAQSSKfDII4/Yb7/9FjmERu2ef/75keeJPNhzzz3toosuiqri5ptvjnoe9hNlsdcx69Wr526tW7d2I5SbN2/uRiMrsebYsWNty5YtYR+6yPXJRPOodu/e3ZYsWZJnPe+9957rSNi2bVsbN25c1LZXXXWVnXfeeW4KyqgVPHECSekB+uyzz9rUqVNt6dKlpszvmk8itnz55Ze2atWq2MU8RwABBBBAAAEEEEAAAQQQQAABBBBIgYACoMFy3XXXuWkNg8sSeaxE2coOr56ZKjNnzrQff/wxaVMjHn744da4cWM3f6mOp6RM7dq100M3NaPmOlWA98UXXzSNStYUjsVdZLR48WLTVAT5lVatWrkAZ6VKlXJsqt6g6j0qA0pOgdADoFdeeWWO7tM5D8sSBBBAAAEEEEAAAQQQQAABBBBAAIHiEvjzzz+j5v5Ur8kOHTqE2pytt97a+vTpY77n5+bNm+3VV1+1U045JdTjBCurUaOGbbvttvbHH39EDQWvVq2aPfnkk26dhopr3lMFFNOhqG0qpUqVKlBzNJw/dttmzZqZbpT4AqEGQNXTUy9qRdD79etn++yzj9WsWTPHRdEL/pNPPrF77rknfqtYigACCCCAAAIIIIAAAggggAACCCAQqoB6G2qYuMrvv/8eNWJXMZ327duHejxVtmLFiqg6L7/8cps0aVJkmbLNa87QMEtscNDXXblyZdt9991t4cKFrjdqugRAffu4T55AqAFQzVWgyW2vueYa0ws6r6IM8WvWrMlrE9YhgAACCCCAAAIIIIAAAggggAACCIQkMGvWLJdAJ151GpquW7KLpkvUzZf169f7h0m/VzZ6DTdXqVWrVtzjvfzyyzZ9+nS33d577+0SDWmOTl/mz5/vhtgfdthhtuOOO5qmEVDW+7322stGjBjhhvf/9NNPbkj7t99+a/Xr17eRI0e63ceMGWMfffSRqWPgGWecYZqTNLYoQK0A8dy5c01zmGpO04IkNfrmm2/cfvvvv3+ORFMLFixw7dRUlJr/VD19u3TpYmXL/hcW1HSW06ZNs4oVK7pz79Gjh2ujb5/21TYzZsxwdakn7+TJk12AW3OPHnHEEX5TF1hXwquPP/44MjVm06ZN7aSTTopsk+oH/51pCEfWhS9fvrzL8lWQ6jRBKwUBBBBAAAEEEEAAAQQQQAABBBBAAIFkCqiH6yWXXOKyxGuuzJ49e+Y43BVXXOEChOrYt3z5cjv22GPtvvvus/fff9+U1Onhhx82xbIU2Pziiy9M2dsPOugg22qrrdx2mldUAVIFTDUyulGjRm5Yug+Aap7Oc845x26//XYXMIwNgN5///0uu7uCmMqdo8zwCiS+9dZb7hg5Gvx/C9QuTUmpjol333131GYKUj7wwAP26KOP2g477OCmAVBws2/fvvbQQw+5bTVC+8wzz3TntdNOO7kgrQKWCpD279/f5fC5+OKL3ZyqCpAq0KtjVa9e3Z5++ml77bXXXLBz1113dfXJb9GiRfb4449H6ldQuTgDoKHO9qrg56mnnupOPEo7lydfffVVLmtYjAACCCCAAAII/H8Bfejr3Lmzu+mDGwUBBBBAAIFUCKiXk3//mThxYioOyTEQQCBkASVduuuuu0zBO82PqQDl6NGj7cMPPzQ/76Y/pIJ46uGoYKWG5KtH56WXXuqCf+rhqKKg4dlnn+0eKxGReosqGKjjKB6moKkPdjZs2NDKlSvntg3+o3pzK2XKlDH11lRAU/dqs9qaX4IktUvB1dii3q4DBw50Bgp+qij4qfle1WZfFKhUTM/3dN1vv/3cXKkKwKpoztEJEya4ILBGfh966KEuqHrDDTe489cIb/n5omkNdAxfTj/9dKtdu7Z/Wiz3ofYA1RkoY1i3bt1c1FjdfPMqysbVsmXLvDZhHQIIIIAAAghkucBvv/3mfkkXgz5UUhBAAAEEEEiFgOYtVE8ulWXLlqXikBwDAQRCFtCQfvWk1JDy7bff3vVKVK/FeEW9FhWA1JBzX/Q5VEUJkzR0XHOLVqlSxS0L9t7UcsXDHnzwQRcUdRsU4R8FK31RAillrFfCqCeeeMIGDx7sV8W9j5cZXr1VFXBt0KBBZB/16pwzZ447H7/wzjvvdHE8nb+K/v5pO82VGizbbLONe9q8efPIYvUUVfFTC+ixcgKpl+tuu+1mAwYMcG7Dhg3TqmIroQdAFUHXi+bII4+0Qw45JO6J/f333zZ79mzT/AQKglIQQAABBBBAAAEEEEAAAQQQQAABBJIroLkkfaBMQ8KD828q4KWh3GGX2OMoyKbehr7klrDIr0/kXhnolehIP6Krh+Jxxx3nhpXrXGOLfvBQT8qzzjoratXw4cOjnuf2REnA1cvxu+++y22TfJcrqXiwHHzwwe7p119/HVxc4Mea81XzmMaW2CCwhurrpu3VG9SvD74+Yuvwz/213LRpk1/keqOq96jmOdUUAgqGalh/cZacVzzB1mi+As0bsGHDBhfgTLA6dkcAAQQQQAABBBBAAAEEEEAAAQQQCEFAiWt8UaKeunXr+qfWuHHjSK/nyMIQHowaNcouu+yySE0333yz+SHlkYVJfKCAq6ZR0nDyN954w/WkHD9+fNQRNYT7zz//tF9++cU0P2hRi4aKq6doWEVzamo4vuotSlEwNnaof7x6dP4nn3yyC0xrvlAFyjXsvqjBaY32VrInJUDX/KkK5CoIetppp8U7fEqWhRoAVWRYk7kq+KkusJoIVtD6BSGIpklilyxZ4iZKTclZchAEEEAAAQQQQAABBBBAAAEEEEAAgYiA5ntUYh8lq1FRwOqTTz6xfffdN7JNog+U7VxZ0oMlmC08uDyZj9WjUcPINWxdgTjNcal5KX3RcHNto2zuyhSvRD/BojiWMtdrSHdeRUPuDzzwwLw2KdS6lStXmm7B4faFqUBTU+q6fv/991FzcqoOBXx1fRS3O+aYY0x5ejSMPRi/K8yxgtvKSoHkd955x9RRUgFv9Qbt2LFjZJ7R4PapeBzdtzbBI+rXA71QNL5/7ty57kV17bXXukxU6jLsb5pwVhOiBuc2SPDQ7I4AAggggAACCCCAAAIIIIAAAgggUAiBE044IWrryy+/POp5ok809+bnn38eqUZzQ8Ybkh3ZIKQHGnavEuyNqU56Y8aMccuVyOi9995zj/0/mspR8/1qLs9gUZBQ2eNr1aoVXJzjsYaPK6h47rnnRtZpePivv/4aea4HmhZSxbfRPcnlHyUq0vGL2mNWU1Oqk6L2170vGq6u6SsV+FUsT8mcatSoEQl+alv1Cg36+X1j7+Nt46cN0JB+BZoVBNd5vP7667G7p+x5qAFQRXcVOe7Vq1cELa8z8Zmz8tqGdQgggAACCCCAAAIIIIAAAggggAAC4QsoOOXncFTtyoI+Y8aMUA6knouxAVWNGk52UW/N1atXu8PEJtBUcFJBXwX41OtRuWl8ueKKK9wIZgUGe/fu7XqM3nrrrS6/TYcOHSLJj/z2U6dOjQQIFdRUkLRTp0529NFH+01csPeHH35ww+4VCB47dqybg1QbKCP9t99+67b1yYeCc31u3LjR7rnnHjcnadu2bd12+kfn98cff7hERcEgqoKvKv5ej31G++nTp7vzUNb2Cy+80DS0XtnglaVew+t1U7Z59dZUzh5tozYpIKxESOodquITwgWP4c/h999/d9voH8236nsW67mCx5p39YADDtDTYimhBkDVTbZnz56ue21BzkZzClAQQAABBBBAAAEEEEAAAQQQQAABBFIvoKQ9yuMSLJq3MRiIC64r6GMF5o4//ng39Nrvo0zk/fr180+Tcq/Rxl27dnW9DXUAZXS/+uqrXcDQH1BBRWVG//nnn12CJAU8VTQdgOZIlYkSAan9Wjdo0CAX2PT7+3tlRNexFNRt3bq17bzzzqagaLBorlHNPXrbbbeZgqjqLakgrLbVcHplW1fRCGkdT4FHBWjVY7N79+4uSKkh+76ol+mpp57qklcpyKuOhRrirgTjEyZMcJspo/u4cePcY/XqVAZ79X7VvhdffLFNmzbNJk+e7M5dG6kXqOZp1Vyjatv1119v//vf/+yCCy5wvUD9VAGax9UneNK6L774wg1xv/HGG92xFDzX/KEqO+ywg2u/Aq5KgjRlyhQXUN59993d+uL4J9Q5QHUCt9xyi4ui9+/f351wXiel+Rf0YqQggAACCCCAAAIIIIAAAggggAACCKReYMSIEfbkk0+a5q9UUU++9u3bu96gChQWtmhqRAU6Naw6WBTIS0aW+eAx1HNTt7yKAn4LFiyIu4mGjGtIuALA6oGpIKXvnRm7Q7t27VzWeOW40dD6YE9av23z5s3dFJGy9Qmn1FtUvSaDc21qnYK3KpqvU23cfvvtfTWR+xYtWrjgrAK0wRI7p2lwnbK7a7i/rquujW9HcBtlvlccTwFaP/+p4nUK7laqVMltqikudQsWBTTnzZsXXOQeK/GRepfqvNX5sTiTH/nGhRoAVfRZmbU0yWrnzp1ddNsfKPb+yy+/tBdeeIEAaCwMzxFAAAEEEEAAAQQQQAABBBBAAIEUCVSpUsXFcg4//HDT3JAqGhquXoPqTageicFgXV7N+uyzz1zwU3lhgmXIkCF5xoiC26bD44YNGxaoGQoWqudofiUYdMxvNHS9evXyq65I69XDM68Sr10++JnXfvHW+UD3jjvuGG91sSwLNQCqCLW6Byvbk4q611IQQAABBBBAAAEEEEAAAQQQQAABBNJXQD0fNf/jKaecEpnXUvM8nnTSSW6kr+bH1PyWmscxXlH2eM2X+dBDD0WGn/vtNNemHybtl2XyvU/6o6Q+lMwRiP/KLWL7ld2pT58+7oWtzF477bRT3Jr0YtHkqrGZsOJuzEIEEEAAAQQQyGoBDT3S5wuVfffdN6stOHkEEEAAgdQJqAeYf/9p0qRJ6g7MkRAoJoGTTz7ZDVvW8PX169dHWjFnzhzr1q2bSwLUsmVLNyxcSXM0lFvDtZU0R8PA45W+ffu6OSAVLyopRXNuqixevLiknFJWnEeoAVCJ6T+M5ktQ1D+vom7V+o9AQQABBBBAAAEE8hLQB20/tKhy5cp5bco6BBBAAAEEQhPQXHs+YQfvP6GxUlGaC2j+TP34rERISnITLOoRqnk9Y+f2DG7jH2votHp9am7JklRuvvlmW7VqlfNRhvnRo0e7If/Kck5Jb4HQA6CaIFcTp+ZV1E1YE84qWxUFAQQQQAABBBBAAAEEEEAAAQQQQCA9BJS13A9pv+mmmwo1eldD5DVn6MiRIy1Zc1kWp5LmMqVkpkDofZCVBatNmzZ5aqjr8yOPPJL07F95NoKVCCCAAAIIIIAAAggggAACCCCAAAI5BJTERlnAv/vuO5s0aZL16NHDckuio2zfBx98sI0aNcolT3r44YdLZPAzBxILMkog1B6gmtszNtNXbhpdu3Z1/4F++OGHAmcTy60uliOAAAIIIIAAAggggAACCCCAAAIIhCugLOcaDq+biqY8VBxnzZo1Vr58eatevbrVr1/fzR0a7pGpDYFwBRIOgGruA817oKIAqIa3a/LbUqVKxb1p7s8ff/zRbrjhBne/aNEi07B5CgIIIIAAAggggAACCCCAAAIIIIBA+grUqVPHdKMgkGkCCQdAFfU//fTT7Z133omcu6L/BSllypSxunXrFmRTtkEAAQQQQAABBBBAAAEEEEAAAQQQQAABBAotkPAcoIr8z5w50y644IICH1y9Q3feeWebOHGiVa1atcD7sSECCCCAAAIIIIAAAggggAACCCCAAAIIIFAYgYR7gOpgyvJ14403uglxr7/+evvoo49ybYOCnzVr1rQKFSrkug0rEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBMARCCYD6hgwbNsz16Nxll138Iu4RQAABBBBAAAEEEEAAAQQQQAABBBBAAIFiE0h4CHxsy88666zYRXGfjxkzxhYuXBh3HQsRQAABBBBAAAEEEEAAAQQQQAABBBBAAIEwBEIPgBa0Uf/884+deuqpBd2c7RBAAAEEEEAgSwU2b95sGzdudDc9piCAAAIIIJAKAd5/UqHMMRBAAIHUCIQ6BN43edKkSTZ16lRbtmyZrVu3zi+O3GvZ4sWL3ReZlStXWrVq1SLreIAAAggggAACCAQFlDTxzDPPdIuuvvpqu+KKK4KreYwAAggggEBSBB577DEbMGCAq3v48OE2YsSIpByHShFAAAEEki8QegB01KhRdtlllxWo5UcccQTBzwJJsRECCCCAAAIIIIAAAggggAACCCCAAAIIFEUg1ADomjVrTD0zLrjgAttjjz1cpvfx48fb2Wefbcr+7suDDz5oTZs2NWWMpyCAAAIIIIAAAggggAACCCCAAAIIIIAAAskSCDUA+vHHH9vQoUPt2muvjbT3r7/+su222846deoUWXbSSSdZhw4d7Msvv7Tdd989spwHCCCAAAIIIIAAAggggAACCCCAAAIIIIBAmAKhJkHSfJ7q+RksJ554ot19993BRe7xoYce6nqL5ljBAgQQQAABBBBAAAEEEEAAAQQQQAABBBBAICSBUAOg9evXt5dffjmqaZUrV7addtrJRo8eHbVcSZBeeOEFlwgpagVPEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBkARCHQKv4ewaBt+sWTOrUqWKnX/++dajRw8bNmyYNWrUyBYsWGBt2rQxZdN77bXXrEyZMvbHH39YjRo1QjodqkEAAQQQQAABBBBAAAEEEEAAAQQQQAABBP4TCDUAWrp0abvjjjusY8eOprk/t912WxcArVOnjikZ0mmnnWYTJ06MHL1z584EPyMaPEAAAQQQQAABBBBAAAEEEEAAAQQQQACBsAVCDYCqca1bt7Z58+bZs88+64KfvsGnnnqqVaxY0caOHWvz58+3Aw44wG688Ua/mnsEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCB0gdADoGqhhrsPGTIkR2N79+5tulEQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFUCISaBCm/Bq9bt842bNiQ32asRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEQhFISg9QtWzmzJnupgRH55xzjmusEh717dvXTj75ZDvllFNCOQEqQQABBBBAAIGSLaDpdcaNG+dO8sADDyzZJ8vZIYAAAgikjUCrVq0i7z8tWrRIm3bREAQQQACBwguEHgDdtGmTnXHGGfbAAw+41hx//PGRAKiSIT3xxBN2zDHHuHlAb7311sK3mD0QQAABBBBAIKsEGjRoYNtvv70758qVK2fVuXOyCCCAAALFJ7DLLrtYz549XQN4/ym+68CREUAAgTAEQg+AjhkzJhL8jNfA7bbbzh555BGrV6+e7bffftavX794m7EMAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIWCDUOUC3bNlio0aNsn322ccmTpxoL730UtwG7rTTTtasWTMbOnSo/fPPP3G3YSECCCCAAAIIIIAAAggggAACCCCAAAIIIJCoQKgB0G+++cYlOdL8nyeddJJpyHtupXHjxvb777/bV199ldsmLEcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBISCDUAqnlR9t5778g8XXm1bPbs2W71smXL8tqMdQgggAACCCCAAAIIIIAAAggggAACCCCAQJEFQg2AKuO7kiDlN6x9woQJtmjRItfoJk2aFLnx7IgAAggggAACCCCAAAIIIIAAAggggAACCOQlEGoAVAfq27evywKvQGhsWb16tV111VU2ePBgt6pFixampEgUBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgGQKhZ4EfMmSInXjiiVa7dm1r1aqVfffddy7g+fnnn5uGvWveTxWtnzJlSjLOiToRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEnEHoAVLUqA/yDDz5oV1xxhf3000/28ccfR7jLlCnjEiSpJ6iywVMQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFkCSQlAFq6dGnr37+/6wn66aefmnp/rly50nbddVfTnJ9169ZN1vlQLwIIIIAAAggggAACCCCAAAIIIIAAAgggEBEINQC6ZcsW69Gjh3311Vf20ksvuR6eBxxwgOlGQQABBBBAAAEEiiKwbNkymzdvntu1cePGtueeexalGvZBAAEEEECgUALLly+3uXPnun323ntv22uvvQq1PxsjgAACCKSPQKhJkBYuXGjTp0833f/444/pc5a0BAEEEEAAAQQyVuCVV16x4447zt2YPzxjLyMNRwABBDJO4PXXX4+8/zz++OMZ134ajAACCCDwn0CoAVD9ItagQQM78sgjrWXLlv8dJZdHr732Wi5rWIwAAggggAACCCCAAAIIIIAAAggggAACCCQuEGoAtFSpUnbffffZ2rVrbcOGDfm27s4778x3GzZAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSKKhDqHKCrVq2yzZs327HHHut6gA4aNMjq1auXo23r16+3l19+2V588cUc61iAAAIIIIAAAggggAACCCCAAAIIIIAAAgiEJRBqAFSN6tq1q61Zs8a1b+DAgWG1k3oQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFCC4QaAK1ataodf/zxdv/997uGbL311la5cuUcjVK2+J9//jnHchYggAACCCCAAAIIIIAAAggggAACCCCAAAJhCoQaAFXD+vTpY7NmzbJXX33VatasmWtbly5dar169cp1PSsQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFEBUJNgqTGtGnTxs4555w8g5/abscdd7TevXvrIQUBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEiKQJF7gGqY+9tvv21///23u2277bZu6Hvp0qWtoHN/Dh48OCknRaUIIIAAAggggAACCCCAAAIIIIAAAggggIAEihwAff31123SpEm2zz772PDhw619+/aIIoAAAggggAACCCCAAAIIIIAAAggggAACaSVQ5ACozkJzfGq+zwoVKqTVSdEYBBBAAAEEEEAAAQQQQAABBBBAAAEEEEBAAgkFQLt06ULwk9cRAggggAACCCRVoGfPnrb//vu7Y9StWzepx6JyBBBAAAEEvEC3bt2sSZMm7invP16FewQQQCAzBRJKglSrVq2EznrYsGEJ7c/OCCCAAAIIIFDyBbbeemuXPFEJFKtUqVLyT5gzRAABBBBICwHef9LiMtAIBBBAIBSBhAKgiX4JmTNnTignQSUIIIAAAggggAACCCCAAAIIIIAAAggggEA8gYSGwCuA+eCDD8arN89l69ats48++siUSImCAAIIIIBAqgR+/PFHmz9/vi1evNhWrVplpUqVsmrVqtmuu+7qhlhrbmsKAggggAACCJQsgS1bttjChQvts88+s++//97WrFljW221lVWvXt123313a968uVWqVKlknTRngwACCCAQJZBQAPTJJ5/SZU4EAABAAElEQVQ03SgIIIAAAgikq8DPP/9s99xzj02ePNkWLVqUZzM1z9dJJ51kp512mpUtm9BbZJ7HYSUCCCCAAAIIJF9AP3refffd9tRTT9mvv/6a6wEVDD3ssMOsX79+1qtXLz4D5CrFCgQQQCBzBRIaAp+5p03LEUAAAQRKusBff/1lF110kdWvX9+uuOKKfIOf8vj444/dPjvvvLNdd9119vfff5d0Js4PAQQQQACBEiegkR5du3a1Zs2a2YQJE/IMfurkN2zYYDNmzLATTjjBdtttN5syZUqJM+GEEEAAgWwXSKh7S//+/e24444rlKGGH6xevdp9ydSXSwoCCCCAAAJhC8yaNcuOP/54W7JkSZGqVvB09OjR7guQeo60bNmySPWwEwIIIIAAAgikVkDv2+eff36Rf8T89ttv7dhjj3U9QceOHZvaxnM0BBBAAIGkCSQUANWvY+3bty9S4/SmoiEJFAQQQAABBMIUePrpp+3EE0+09evX56h2v/32s3bt2lnjxo2tRo0aph/lNET+k08+cT0/ND9YsHz99dfWpUsXe+KJJ6xz587BVTxGAAEEEEAAgTQS0Hv64MGDLV7QUtPaHH744XbooYe6Hp5Vq1Z1nxM0H+js2bPtxRdftF9++SXqbNQLdMGCBTZx4kSrXbt21DqeIIAAAghknkBCAVAlkEikHHXUUYnszr4IIIAAAghECTz33HOu5+emTZuilnfo0MFGjRplTZs2jVoe++S9996zYcOG2VtvvRVZtXbtWuvZs6e98sordsghh0SW8wABBBBAAAEE0kfgf//7X47gp+b2PPPMM+3SSy91P3zm1lp9btBc4ZoyR0FRXzR3uEY8Tps2zSpXruwXc48AAgggkIECCc0BunTp0oROWUMTKAgggAACCIQh8Pnnn1vv3r0tGPzUlxV9aVHPjvyCn2pDq1at7M0337RHHnnEypcvH2mWepP26NHDEn3fi1TIAwQQQAABBBAITeDee++12267Lao+jfZQ1vdbb701z+CndlIP0b59+9qXX35pZ599dlQ9mk904MCBtnnz5qjlPEEAAQQQyCyBhAKgzzzzjP3xxx+Zdca0FgEEEECgxAnoS0mfPn1szZo1kXOrU6eOffDBB9atW7fIsoI+UF0KmlarVi2yy4oVK1x22MgCHiCAAAIIIIBAsQtozs7zzjsvqh2a7kajOho1ahS1PL8n+vFz/Pjx7hbcVnWNGzcuuIjHCCCAAAIZJpBQAFRD4Js0aWJ33nlnkRNNZJgXzUUAAQQQSEOB++67z+bOnRtpWcWKFW369Om21157RZYV9sH+++9vSqRQpkyZyK6vvfaaaY5RSmoFPv74YxszZoy7vf/++6k9OEdDAAEEEEhrgQsuuMDWrVsXaaPe+5966qmEhqyrF6imxAmWkSNH5pgnNLiexwgggAAC6S1Q5ABor1697LLLLnMZ8j799NMcv5Kl92nTOgQQQACBkiLwzz//uPk9g+dz0003WfPmzYOLivS4devWNmTIkKh9r7nmmqjnPEm+QDAAql69FAQQQAABBCSgIe5Tp06NYJQrV879UBnGfJ3XXnutNWzYMFL333//bbfcckvkOQ8QQAABBDJLoMgB0O7du5t+BVOPjDvuuMP0ZZOCAAIIIIBAqgWUnGjJkiWRw+6+++52xhlnRJ4n+kDzfgWzv3700UcuY2yi9bI/AggggAACCCQmoLk/lf3dF73/63NAGKVUqVKmTj/Bcv/990fNNR5cx2MEEEAAgfQWKHIANL1Pi9YhgAACCGSLwJQpU6JO9ZJLLnHJDKIWJvCkUqVKFpu0L/aYCVTPrggggAACCCBQRAENdfdFU9Yo23uYZdddd42q7pdffrF33nknahlPEEAAAQQyQ6BsZjSTViKAAAIIZKKAEhOsXLkyqU2fMWNGpH59+WnQoEHUfKCRlYV88Ndff5mGu6nstttuUXu/8MILdtxxx0UtC/tJ9erVrV69emFXS30IIIAAAgjkK7BhwwbTNGeJlI0bN5pyRiihoLKsh12WLl1quvmi3BTLli1zN78s0fvvvvsuRxWTJ09OaH7RHBUmYUHdunWtVq1aSaiZKhFAAIHMFQj/nShzLWg5AggggEDIAkog8OSTT4Zca+7VaT7QNm3a5L5BSGs051gYc4zm1ZxBgwa5JIN5bcM6BBBAAAEEkiGwfPnypL/Phd3uefPmpaTN99xzj+mWzkVT1SlfBwUBBBBA4D8BhsD/Z8EjBBBAAAEEEEAAAQQQQAABBBBAAAEEEChhAgRAS9gF5XQQQAABBBBAAAEEEEAAAQQQQAABBBBA4D8BAqD/WfAIAQQQQAABBBBAAAEEEEAAAQQQQAABBEqYAAHQEnZBOR0EEEAAAQQQQAABBBBAAAEEEEAAAQQQ+E+AJEj/WfAIAQQQQCBkAWVK33fffUOuNbq66667ztauXesWli9f3i699FIrXTrx3/eUAV4ZbFVU3w033GCbN292z3fYYQc7//zz3eNk/bP//vsnq2rqRQABBBBAIE8BZW6/5ppr8twmv5WbNm2ydevWWaVKlaxMmTL5bV7o9T/99FNUssC99trLTjjhhELXk9cOc+bMsenTp0dtctBBB1mnTp2ilqXbk7Zt26Zbk2gPAgggUOwCBECL/RLQAAQQQKDkChxzzDGhBCPzElLW16lTp7pN1q9fb61atbIjjzwyr10KtO7PP/+0NWvWuG1fffXVSPBTC7p162aXX355gepho8QFtt56a6tTp46raJtttkm8QmpAAAEEEMhToEqVKgm/z23YsMFWrFhh+tGwXLlyeR6vKCsVYJ04caKtXr3a7f7DDz/Y0KFDrWLFikWpLu4+jz76qL388ssukOs30DF69uzpn3KPAAIIIJAhAol3kcmQE6WZCCCAAAIlU6Bz585RJ3bLLbdEPQ/jyfjx46Oq6dKlS9RzniRXQF801QtHtwEDBiT3YNSOAAIIIJARAmXLlrX27dtH2qpA6L333ht5HsaDFi1a2JYtWyJVaaRJGD+yRirkAQIIIIBAygQIgKaMmgMhgAACCCRD4Nhjj7Vgr8AXXnjB3njjjdAO9fzzz9usWbMi9dWoUSPth75FGssDBBBAAAEESrDAqaeeGnV2Grbve4RGrSjik5EjR5qmxPGlR48eVrVqVf+UewQQQACBDBIgAJpBF4umIoAAAgjkFNAwvUGDBkWtOP300+3333+PWlaUJ8uWLbPLLrssatf//e9/ttVWW0Ut4wkCCCCAAAIIpF6gY8eOts8++0QO/Ouvv9rAgQMjzxN58Nxzz9lDDz0UVcVFF10U9ZwnCCCAAAKZI0AANM2vlebO0eThFAQQQACB3AWU+Kh69eqRDRYvXmy9evWK6rURWVnAB5oDVD1Lfvnll8geu+yyS9KTH0UOxgMEEEAAAQQQyFOgVKlSdvPNN0dto3k7r7766qhlhX3y8ccf24knnhg1/F3PmzZtWtiq2B4BBBBAIE0ESIKUy4WYMmWKKZnGSSedlMsWyV+s4Rt6o1UW4gcffNBq1apVoIOq3bfeeqtrf3AHZTHedtttXU+p2EyMX3zxhUsiooCrL5rjZvDgwab74izvvPOOG87qsy8H26IPPer9td1227nbnnvuaQ0aNAhuwmMEEMgCAf1tmzBhQlRSgpkzZ9qhhx5q06ZNiyTQKSiFAqiaW1R/G33R39D77rsv1OQKvm7uEUAAAQQQQKBoApqTU/ND33PPPZEKrrzySlOW+HHjxhU6AdOzzz7rvgMGh9LXrl3bRo0aFamfBwgggAACmSdAD9A410wTXT/11FP25JNPmrILFlf57bffbOXKlS4LsYZhFrQoYHnuuee6gOArr7xi/qYPB2effbbFBj9V7+67724XXnihKXuitpfBOeecU+zBT7WtdevWLqOjroU/lwULFljjxo2tSZMmro1ffvml3X///XbKKae4oPHdd98d6vw/agcFAQTSW0AZ5y+++OKoRs6ePdv9nRg7dqwFf+CJ2ijwRD3uR48ebc2aNYsKfmoTLT/88MMDW/MQAQQQQAABBNJB4LbbbrOWLVtGNeWuu+6yAw44wGbMmBG1PLcn+h6kkR/dunWL+h6hrPIKrm6//fa57cpyBBBAAIEMEKAHaJyL9OGHH9rSpUvdmtdff92OOuqoOFuFt0iBzgoVKkQl8VDt9evXt2uvvdb15Nxvv/0KdcBKlSrZWWedZTqXJUuWuH3VSzKvosCoMh3q3BUMVR3pUvTBo0OHDqYeXSrt2rUzBTuCRT1l9Yvtww8/bI888ogpEcrQoUPtkEMOCW7G4xAE9JrSUGAKAukmcN1115mGrt95552Rpulv7Pnnn2833XST9enTx/390A8oO+ywg/uxR/OFaaibviBNnjzZli9fHtnXP9CcX/q7SEEAAQQQQACB9BPQdwXN2ams8HPnzo00UO/v+g6h7zgnnHCCGxmy2267ue9d+u6goKd+LH3mmWfs6aefzjF1jupVZnn9MEpBAAEEEMhsAXqAxrl+6v3pe0k+8cQTcbYIb5F6NQ4fPjzXZB0avqlgn4Z6F7ZouGbfvn0ju+lDQX5FAdOuXbumVfDTt7ly5cr+YdyhLOXKlXNBUQUwdtppJ1uxYoVLXvLuu+9G9uNB4gIffPBBjgnhE6+VGhAIR0B/K++44w7XW9P/Hfc160uOAqTqxalM7lpftmxZN72IvjBpDrHY4KeSHann51VXXeWr4R4BBBBAAAEE0lBAPTTVWaJ79+45Wjdr1ixTEsP999/f9J1CnwH0Hr/rrrta79693Q+gwWzvqkDfJ1566SU77LDDctTHAgQQQACBzBMgABpzzdT78aOPPrIzzjjDrdH8b5988knMVuE91dCMTz/9NLwKY2rSF/2aNWu6pXoD/+uvv2K2+O/pd99954Z8Hn300f8tTKNHBQ0Cq+eqJj7XhxoN5R8xYoSbAyiNTiVjm6Lg0DXXXBM1IXzGngwNL9ECGgqv+YM1TUZuRX8fdMutNG/e3PUkD/6QlNu2LEcAAQQQQACB4hdQcHPq1Kmu12ZeQ9bj5Rbwrdd3jv79+7vvhPosQEEAAQQQKBkCBEBjrqOGPhxxxBGuJ+E222zj1hamF2hs7yENFQ5OoB08nIZpP/bYY26R/yIe78u4hmdoSGdRin7dPPbYY92umttOwztyKy+++KLLbLjjjjvmtonpw4LO6f3333eB23TNUN+wYUOX7EknojYqCUpu5Y8//rAff/wxz0CI9tW5r1mzJlKNrpUmV483r6CWqc5//vknsn1eDzQEV3XFu/657ae2KHiuwHVec9Uq6O0D36r/22+/zXEcvb4+++wzU29ZtSNe0Wtbv5z716LqKkx749XJMgSSKaC5wObNm+f+zrZq1apAh9KXHvX0mD59ur366qu21157FWg/NkIAAQQQQACB9BE47bTT7JtvvnGJizStWEGKOlHoR091flHSw/ymDytInWyDAAIIIJA+AswBGrgWCpRp3khNoq05OZUB+PHHH7e3337blIRI2f/iFSXkefPNN91Nb5wPPPCA+8KtofQKGikI2bNnT5eYyO+vQOukSZP8Uxs4cKBpyLqSEd1yyy0u2KY5azSMQ1/CzzvvPDd/TWSHQjxQj061SQEzZbc/7rjj3LDPYBUK1KmHqJIk5Vb0YWDMmDGm4KImFFfwTfPq9evXL2qovYaZ6jgKqPoAoObr1AcKWSlDvQJ+fi6efffdN7dDJrRcyZOU+ERFbRk0aJAz9pUq+Kxz1hBYfUDSEHq18fjjj4/yUcBQcwO+/PLL7lyVTEr16porsKiepkOGDHGvFwW71atXryMFrjVvkOrTh7DYsnbtWrv99ttNQ8qVvV6BR/VA1hxDgwcPdsNyY/fRc/VQ1jHq1Klj1apVszfeeMOdl+Z89UN0FKzVdmq35rG9/vrr3TQKGsqr48hGQ4HVRs2VqIC2pj5Q0F9zqOo8NBy4Vq1argk6T/Wo8wF+nbv+f6ioty2/jjsK/klDAf1d1f9B3fRjgf6eKiiqLO+rVq1y/y/0/0h/1zQsTv+/69at687EB/vT8LRoEgIIIIAAAgjkI1ClShUbNmyYXXLJJW5eUH1m1g/+33//vftepM/w1atXd9+/Dj74YGvbtm2OnAz5HILVCCCAAAIZJEAANHCxFCzaeeed3ZugFvfo0cMFQBVMUjBTWdFji3r6LVy40AWhFCStV6+ejRw50gU9FZDSEEx94VYvUiXd8AEqBQTVI8n3zlSAMhhgVbZzBQs1hCPRoqCsgqAK+CnwGC+xkyb/1rm0adMm7uEU1LzgggtMvUOvuOKKyDYKoCkrogJlPlmU5stRT0Et01x8Kpp8XENSFDhVQEKJSJRlMZlFx1cAUoHtlStXunlWlfRE11PDuPWhR7/uKkCtQIiCzBMmTHAfivRhSWX+/Pn2/PPPu+zz2u/zzz93QWkF/DS0/o1/P0gp8ZKSqyhooknS9957bzdn4KJFi1z9Dz74oO2xxx6mD1a+KNApTw3NeeihhyIfthQMVaBRAZorr7wyah/tq9eRgss33nije61qmaY5UHBX22+99dYusKz5XrWdgrcqX3/9tWu3gqY+0KrlCkarV/CoUaMiyaL04U+vF11DzaeqHnEKjKqd2k7BZAWJLrvsMlWRZznooIMiPUZjN9T/B71+9P8m7JKMOsNuY0HqU/KeTC8///xz2pyC/s936tTJ3fJqVLzXj/6G6JbJJZNfT/ox0Ce2Oumkk1yG3ky+FiXh9SR/vb/qluqiY/ofWItybI2a0C3e//Wi1BfcJxl1ButP1eNM/nvhjdLp/ce3qSj3JeHvRTq8nvQ5Xe8feRV1AIgduacf/dVhQUVzhcYmYc2rvnRcVxJeT3LN1PefdHxN0CYEskmAIfCBq60gp4KevuiN8sADD3RPFVBSj73Yoi/UCmKqd5GKhj0rEKgAmoKdChZqGKaKJt/OrcTOb6mEHOpVqJ5JYZRevXq5QJ/qUq/W2KIeizqmzide0dBovdEowBYsChirvPXWW8HF7rE+JPjenf6Lq4aia05S9RpNdpFpgwYNIof55Zdf3GMF8BTwVUZnBT9VqlatGvlQJAsFIFWaNm1ql19+eaQeBRIVaNQHKPVgVR2NGjVyX6TUo/LSSy+1M88806075ZRTrGPHjq6eWB/1yNSXJGWp91MtaEO9VhQYlrV6ayow64vmox0/fryr37trnQKumsBdw9HV61NFvTkV3PRFPV3Va1W9ixXkVcBWRcF5lWAbFKjWkB8FaX0A1W0U+Cf29RpYFfXQf8mMdx+1IU8QQACBPAQUyNAPirrpbxMFAQQQQACBVAio84h//1GHEAoCCCCAQOYK0AP0/67d3LlzXQ9B30PTX1L9yqfM6Bo+rp6AvsemX+/vNcRCRUPnFRgLFvV0e++999zwy+Dy4OPcAkoKOIbR60hBR52bAl5ffvml69mo4J6KfunUG7t6L+ZWlI1eQ+DVkzNYfMBUGddji85JgWAFAmWoHoQKgN59991FymofW39BngcDyAoqrl+/3vVY1TXRcNhgURDUFwVANRTdF2WMVvBTAfFgnTpH1fXVV1+5YK96WAaLApMqPviqx2/822tUgUr1pg0GaLVORUF49RrVdZeZeqaqaEi62qgemrFFGap1DdW7zRcFMfV6VEZLZcPUEH8VBUx9Of30011b9tlnH7/I3Qeva7w25vZ6jark3yf6EcH/34hdp6kHVHwQOnZ9UZ6rl64CwWHWWZR2JLKPPwfVod7SBbVO5JjJ2FfXQefCtUiGbuHq9Ncik19Pwf8Hepypr6uScC38OehVqGuh11WqS/D1kMixw3wd+b/dYdaZyLkVZV9/Dto3k/9e+NdoSbkWxfX/rCivodh9/LXI5NdT8Jx4/wlqpP6xfz3pyMX1/yKs95/U63FEBBCQAAHQ/3sdqPdnly5drHz58lGvDPXI88OGNaRYc3kW9sO+D6zF60EadbAkP1GPTN/jT8PhfQBUw+3VizFeoMs3SQYa/uyLzkX7qbekSrxEQFquIN+AAQPc0BEFWBUQ1Vw7qSoKWvqi4eaa61PBWs1lqbkuY4uGk6tsu+22UavyerOL7RUb3NHXE7z2vpdmbLDU76fXi6YK0HB4TYOgokRNmmpBwct4bdlll11Mt9ii6Q8UAFUAN15RQD8Y1NcQf13T33//3W2e23WNV1e8ZXkln9J8TAoc59a2ePXlt0zBfFmHWWd+xwx7va61rpmKgtg+cB32cZJdn66DejBr2onC/s1MdtsKWr/mAPWJzxTI15QamVj0/1h/9zL59aQfc3zRDzSZ+n9cw7b1g1hJeD3peui6BH8U9Nco2fc6biLBLf0Ap1uYryO9/+jvRZh1Jtsxtn7ef2JFiu+5Xk8+gaU+F/L+U3zXImjP+0/xXQcd2X+e0eNMff9R2ykIIFB8AgRA/7VXMExDvPXlUAmPYovvgam5E7XdIYccErtJws/jBbXyqlRDyOMFpzQ3Y//+/ePuqgRL++23n+vx53ukaii1hoR369Yt7j6xCzWHj7LX6wuchlmrh6wCc3kVJV1S4FVDuJXYKdhLMa/9El2n4Iuf/0kfXhRw1PynKgr+KoFTGMX3loxXl7+u6lXhi4KwKrn1jNQ6BTMVAF2yZImeRu6DH8LcihD+0a+pmt9IWa+V8Vo9djVdg6ZzyK3488ptPcsRyEQBTdUQ/L+qv7H+76zuEwm4BD0UEPY9oIPLeYwAAgggEF9APx4UdN5X/7db90X5AS6vz3XxW8dSBBBAAAEEEMgEAQKg/14l9VLTcOfckvKoJ5MS0+iLsZLQpEMANLanqn+x5fcFXb1AfQ9EnYvmBtVQ8COOOMJXkeu9pgDQHJJyUoIcFWVGz6+oN6EfIq/ehRqK7+dFzW/fRNZrqL8vSjilD7T+13Rlf0xlCQYM/TXKa0J434PEf3D3PTLDnntIPdwuuugi56LESsFEXHn5BM8nr+1Yh0AmCShRWV5zNYd1LppSRD86UBBAAAEECiagUSN+/vKC7VH0rfTDMAUBBBBAAAEESp5A1gdANdRUWbCV0don7Il3mRX01BdWBQ81bFdDxsMshQ0o3XXXXUU6vAKBytKuQJoS42hOzHbt2uU7tEY9OJWURwme8sugGNsw7XfyySc7NyWTUqBNw8/zGjoeW0dhnytYPXXqVLebbE844QT3WEPyVZShXYFtDRGPVxScVDKg3ALN8fYp6LL69evbnDlz8uxh6XudaVsV3271aNWw/oYNG8Y9nOY5LWgvUV37wYMHu+vy6KOPFjj4qQMX9vUat7EsRAABBBBAAAEEEEAAAQQQQAABBFIgUDoFx0jrQ6hHonoG5tcjUT0lfVHPybBLQYf1JHpcBa58xnoF2WbMmOHmPs2vXs1/qqIh9IUp6l2rQKMS+5x77rkuA7yGz99xxx2FqabQ2yoDup/OQEPwNfxfRUmJ1KtScytpHtR4Rb0iR44cWaRhU/Hqi13mEw4pq+Rnn30Wu9o91zqVJk2auPt69epFht/ec889LtGPWxH4R71E80pkFdjUPdR0BArma45GH2CN3Sa356l6veZ2fJYjgAACCCCAAAIIIIAAAggggAACBRXI6gCo5nt7/PHHTfNm+mHJucEp8OeHB6s3ZDCrt/bJa7iMjhOv+AQ5WucDXnocnIPOB5pyq0PbF7Yok7tPzKSA4J577plvFQqUqbz22mtR22r+UBWftCW4UnNIKuO7pg9Q4FW9LYcMGeI2Ua9b9YIsTIl3jNj95aQgoO/9qXlKzzrrrMhmSsCkHq8qEydOdNMfRFb++0A9LNUbuH379lHJZ/K6vupJqeJ7bbon//eP38/fa3Hbtm0jgU3NuxlbtO3777/vEkv43rbq1ekD15q/VQFaf1ztryH9I0aMiPR09XX6427cuNEvitz7a6rerp988klkuQKjfv7PWHMlklLJ7fUaqYQHCCCAAAIIIIAAAggggAACCCCAQJoIZHUAdPLkyabERttss02BLocfjqwg23333Re1jw8IKQNo7NyOy5Ytc9tq2HkwaKXh1TVr1nTrNCRcw+vHjx/vMpVroQJqysipEhtwdQuL+I+O2717d7e3AoQFKXvssYfbbO7cuXbddde5IexDhw6NTAWggNmkSZMivSoVOLvyyitdz08lWvJFQ/B9D0gF8XySIr8+r/vFixdHVn/66afORp469vz58009I9VT96GHHnLB6uHDh9uFF16YoyenkkQpkKeg4JgxY6xPnz4uoHjaaae5/TW8vGPHjpFj6Xr7RE/x5uD011cZ1GOLb7P280FIBYM176aSbmkaAt2CRb2S9XpS24OvTc296l+DmntVbRw0aJBLeqVz6tKli+vN6evSa8a/fmbPnu0XR+79NdUCTVOg5Fa6V6/dxo0bu+2eeuopU+9fJbBS0fQJKvJ+5ZVX3LQQCnJTEEAAAQQQQAABBBBAAAEEEEAAgXQVKHPVvyVdG5esdilIec4557jh3zqGAlcKhO69995x509Uj7gHHnjA3nrrrUjvTPWe+/DDD90+Cvw9/fTTkR6ASr6jDN9KZHPzzTeb5r1UT07dFEhSQFDDjlV85nkFyBSkGjBggBuu/eSTT7pj+oCbAmlK4KNeqHllD3eVFuCfXf7NMq6kRuqdWZB5LjU/6vLly12w0QeNFXxT8iT1HlQQUq5KsrTk38zlV199tTtX9bhUoM0H8mQpRwXmNF/lu+++6+YCzWtOVW0zYcIE16vT92jU8TRvpQLH8tW1UOD5gAMOsBNPPNElacqtTs3tqURMCqJqH2WLl68Czgrqar5SX3RuCvD5AKgCthpCr/PSkHO9LjSNgNqlYfUKOqpnr0zvvPNOUw9Z1avgp15nmvdUw9nVA1c9cZWASlMqKDP8ypUrTcFPBRZvuOEG23///X0z3L2yRnfq1Mk0RF/bq04FSnU+11xzjR100EGR7VWngvQ+MP/tt9+af13WqVPHbad7tUfXS0Fctb9FixYuqFqtWjWXDEbXqUGDBq7XrAK3arusVa+uo66r/i/5ZE2RBhTggXpSy9D3bC3ALvlu4q396y3fHdJwA/144Ht8q9d0fr3T0/AUXJP0+tQPFLoWeu1kQlHvcf1tSXbRe0BuSfeScWy99+jvbSa/njRiQO9ZKvr73aZNm2RQJb1OvVfovbJChQpRowySfuAQD+BfT6qyXLly7lxCrL5AVelHQL0XHnvssQXaPnaj119/3b1f+/nBY9cX5TnvP0VRK/g++mykETCpKOnw1UivJ91USsLfi0x+/5k3b549++yz7lrovUfvQZlYeP8J56rpb5G+H2mKNQoCCGSeQKl//xiS6rCYr5sCmwooKSiZykCBgmK+R2FBCfQHXwE3BeOCRcEzLc+0oi9QSiqkHqGaB7M4gk0KfCo4qblSNVepn2ohL0sFyBQ8VQA9OJVCXvvktk5fZtUGP7zdb6dAnK5z7LXWnwwdW9sncs2V0VU/JKiXaVhFr0M5+p7VYdWbynr0f8xPPaAfShRgyMSi66AfF3QtihIgL45zPvDAA0tkFnh9iV6xYoX74S1TX08KeOmHMBUFrYoa+CqO11XwmPp7qx/K9CNTQRPWBfdPh8f+9aS26Bx0Lqku+rFy6dKlbhqjohxbU91oDu5409AUpT7to/cfBbdr1apV1CqKfb90fv/RqJhsygKv15O+H6iUhL8Xmfx5Rj/Y+9wFCnqps0cmFt5/wrlqGsWnjig+P0Y4tVILAgikSiA6ipWqo3KcKAH1kCqOHmuFDX6q0bkF2xIJhEVhpPiJetM2a9YsxUeNPpw+2KrnZWGKgpKavzWMoqBvbPBT9arHQbyiIL2C9RQEEEAgVQKtW7eOTJ+Sqe83qbLiOAgggAAC4Qlo+i6NElTh/Sc8V2pCAAEEikMgq+cALQ5wjokAAggggAACCCCAAAIIIIAAAggggAACqRMgAJo6a46EAAIIIIAAAggggAACCCCAAAIIIIAAAikWYAh8isE5HAIIIIBA4QUWLFhgY8eOLfyOhdhD8yKnomjO34EDByb1UEOGDHFzCif1IFSOAAJZJaD5v0eNGlUs5/zBBx+k7LjJ/vuc24k0b97cJUPNbT3LEUAAAQQQQCAxAQKgifmxNwIIIIBACgS+//57u/vuu1NwpOQfYvny5Uk/FyVpUFI1CgIIIBCWgBJ4lZS/w3mZFNc5KiHlgAED8moa6xBAAAEEEEAgAQGGwCeAx64IIIAAAggggAACCCCAAAIIIIAAAgggkN4CBEDT+/rQOgQQQAABBBBAAAEEEEAAAQQQQAABBBBIQIAAaAJ47IoAAggggAACCCCAAAIIIIAAAggggAAC6S1AADS9rw+tQwABBBBAAAEEEEAAAQQQQAABBBBAAIEEBEiClAAeuyKAAAIIpEbgiCOOMCUPSmbp2LGjzZ8/P5mHcHW3bNnSpk2bltTjbLfddkmtn8oRQCD7BJo2bZr0v8O5qY4YMcLuvPPO3FaHujzZ7zW5NbZChQq5rWI5AggggAACCIQgQAA0BESqQAABBBBIrsBWW21lNWvWTOpBypUrl9T6feWpOBd/LO4RQACBsATKli2b9L/DubV16623zm1V6MuT/V4TeoOpEAEEEEAAAQQKJMAQ+AIxsRECCCCAAAIIFJfApEmTbOedd3a3sWPHFlczOC4CCCCAQJYJPPbYY5H3n5tvvjnLzp7TRQABBEqWAD1AS9b15GwQQAABBBAocQL//POPbdy40Z2XHlMQQAABBBBIhcDmzZt5/0kFNMdAAAEEUiBAD9AUIHMIBBBAAAEEEEAAAQQQQAABBBBAAAEEECgeAQKgxePOURFAAAEEEEAAAQQQQAABBBBAAAEEEEAgBQIEQFOAzCEQQAABBBBAAAEEEEAAAQQQQAABBBBAoHgECIAWjztHRQABBBBAAAEEEEAAAQQQQAABBBBAAIEUCBAATQEyh0AAAQQQQAABBBBAAAEEEEAAAQQQQACB4hEgAFo87hwVAQQQQAABBBBAAAEEEEAAAQQQQAABBFIgUDYFx+AQCCCAAAIIpL3Aqaeeau3atYu0c/369bZhwwb3vEKFClauXLnIukQe1K9fP5Hd2RcBBBDIOoEjjzzSypcvX6Dz3rhxo/3999+2zTbbWKlSpQq0DxshgAACCCCAQMkXIABa8q8xZ4gAAgggUACBQYMGRW31559/2po1a9yyatWqWcWKFaPW8wQBBBBAIDUC7du3N90KUtauXWurVq2ymjVrWunSDHYriBnbIIAAAgggkA0CfCrIhqvMOSKAAAIIIIAAAggggAACCCCAAAIIIJClAvQAzdILz2kjgAACCCCQKQItW7a00aNHu+YecsghmdJs2okAAgggkOECBxxwQOT9p3Xr1hl+NjQfAQQQyG4BAqDZff05ewQQQAABBNJeYLfddnPDWdXQypUrp317aSACCCCAQMkQaNSokdWoUcOdDO8/JeOachYIIJC9AgyBz95rz5kjgAACCCCAAAIIIIAAAggggAACCCBQ4gUIgJb4S8wJIoAAAggggAACCCCAAAIIIIAAAgggkL0CBECz99pz5ggggAACCCCAAAIIIIAAAggggAACCJR4AQKgJf4Sc4IIIIAAAggggAACCCCAAAIIIIAAAghkrwAB0Oy99pw5AggggAACCCCAAAIIIIAAAggggAACJV6AAGiJv8ScIAIIIIAAAggggAACCCCAAAIIIIAAAtkrQAA0e689Z44AAggggAACCCCAAAIIIIAAAggggECJFyAAWuIvMSeIAAIIIIAAAggggAACCCCAAAIIIIBA9goQAM3ea8+ZI4AAAggggAACCCCAAAIIIIAAAgggUOIFypb4M+QEEUAAAQQQQCCjBX777Tf77LPP3Dnstttu1rBhw4w+HxqPAAIIIJAZAitWrLBPP/3UNZb3n8y4ZrQSAQQQyE2AHqC5ybAcAQQQQAABBNJC4IUXXrDOnTu726OPPpoWbaIRCCCAAAIlX2DGjBmR959HHnmk5J8wZ4gAAgiUYAECoCX44nJqCCCAAAIIIIAAAggggAACCCCAAAIIZLsAAdBsfwVw/ggggAACCCCAAAIIIIAAAggggAACCJRgAQKgJfjicmoIIIAAAggggAACCCCAAAIIIIAAAghkuwAB0Gx/BXD+CCCAAAIIIIAAAggggAACCCCAAAIIlGABAqAl+OJyaggggAACCCCAAAIIIIAAAggggAACCGS7AAHQbH8FcP4IIIAAAggggAACCCCAAAIIIIAAAgiUYAECoCX44nJqCCCAAAIIIIAAAggggAACCCCAAAIIZLsAAdBsfwVw/ggggAACCCCAAAIIIIAAAggggAACCJRgAQKgJfjicmoIIIAAAggggAACCCCAAAIIIIAAAghkuwAB0Gx/BXD+CCCAAAIIIIAAAggggAACCCCAAAIIlGCBsiX43Dg1BBBAAAEEECgBAl27drU99tjDnckuu+xSAs6IU0AAAQQQyASBzp0728yZM11Tef/JhCtGGxFAAIHcBQiA5m7DGgQQQAABBBBIA4Fq1arZ7rvv7lpSuXLlNGgRTUAAAQQQyAaBqlWr8v6TDReac0QAgawQYAh8VlxmThIBBBBAAAEEEEAAAQQQQAABBBBAAIHsFCAAmp3XnbNGAAEEEEAAAQQQQAABBBBAAAEEEEAgKwQIgGbFZeYkEUAAAQQQQAABBBBAAAEEEEAAAQQQyE4BAqDZed05awQQQAABBBBAAAEEEEAAAQQQQAABBLJCgABoVlxmThIBBBBAAAEEEEAAAQQQQAABBBBAAIHsFCAAmp3XnbNGAAEEEEAAAQQQQAABBBBAAAEEEEAgKwQIgGbFZeYkEUAAAQQQQAABBBBAAAEEEEAAAQQQyE4BAqDZed05awQQQAABBBBAAAEEEEAAAQQQQAABBLJCgABoVlxmThIBBBBAAAEEEEAAAQQQQAABBBBAAIHsFCibnafNWSOAAAIIIIBApggsXLjQnnvuOdfcww47zNq0aZMpTaedCCCAAAIZLLBo0SJ79tln3RnovUfvQRQEEEAAgcwUIACamdeNViOAAAIIIJA1AnPmzLERI0a48y1TpgwB0Ky58pwoAgggULwC8+fPj7z/DB8+nABo8V4Ojo4AAggkJMAQ+IT42BkBBBBAAAEEEEAAAQQQQAABBBBAAAEE0lmAAGg6Xx3ahgACCCCAAAIIIIAAAggggAACCCCAAAIJCRAATYiPnRFAAAEEEEAAAQQQQAABBBBAAAEEEEAgnQUIgKbz1aFtCCCAAAIIIIAAAggggAACCCCAAAIIIJCQAAHQhPjYGQEEEEAAAQQQQAABBBBAAAEEEEAAAQTSWYAAaDpfHdqGAAIIIIAAAggggAACCCCAAAIIIIAAAgkJEABNiI+dEUAAAQQQQAABBBBAAAEEEEAAAQQQQCCdBQiApvPVoW0IIIAAAggggAACCCCAAAIIIIAAAgggkJAAAdCE+NgZAQQQQAABBBBAAAEEEEAAAQQQQAABBNJZoGw6N462IYAAAggggAAC5cuXt6pVqzqIChUqAIIAAggggEBKBLbaaivef1IizUEQQACB5AsQAE2+MUdAAAEEEEAAgQQEjj/+eOvQoYOroXLlygnUxK4IIIAAAggUXKBXr17Wrl07twPvPwV3Y0sEEEAgHQUYAp+OV4U2IYAAAggggAACCCCAAAIIIIAAAggggEAoAgRAQ2GkEgQQQAABBBBAAAEEEEAAAQQQQAABBBBIRwECoOl4VWgTAggggAACCCCAAAIIIIAAAggggAACCIQiQAA0FEYqQQABBBBAAAEEEEAAAQQQQAABBBBAAIF0FCAAmo5XhTYhgAACCCCAAAIIIIAAAggggAACCCCAQCgCBEBDYaQSBBBAAAEEEEAAAQQQQAABBBBAAAEEEEhHAQKg6XhVaBMCCCCAAAIIIIAAAggggAACCCCAAAIIhCJAADQURipBAAEEEEAAAQQQQAABBBBAAAEEEEAAgXQUIACajleFNiGAAAIIIIAAAggggAACCCCAAAIIIIBAKAIEQENhpBIEEEAAAQQQQAABBBBAAAEEEEAAAQQQSEcBAqDpeFVoEwIIIIAAAghEBGbOnGn9+/d3t2eeeSaynAcIIIAAAggkU+Ctt96KvP9MnTo1mYeibgQQQACBJAuUTXL9VI8AAggggAACCCQk8MMPP9hLL73k6mjVqlVCdbEzAggggAACBRVYunRp5P2nRYsWBd2N7RBAAAEE0lCAHqBpeFFoEgIIIIAAAggggAACCCCAAAIIIIAAAgiEI0AANBxHakEAAQQQQAABBBBAAAEEEEAAAQQQQACBNBQgAJqGF4UmIYAAAggggAACCCCAAAIIIIAAAggggEA4AgRAw3GkFgQQQAABBBBAAAEEEEAAAQQQQAABBBBIQwECoGl4UWgSAggggAACCCCAAAIIIIAAAggggAACCIQjQAA0HEdqQQABBBBAAAEEEEAAAQQQQAABBBBAAIE0FCAAmoYXhSYhgAACCCCAAAIIIIAAAggggAACCCCAQDgCBEDDcaQWBBBAAAEEEEAAAQQQQAABBBBAAAEEEEhDAQKgaXhRaBICCCCAAAIIIIAAAggggAACCCCAAAIIhCNQNpxqqAUBBBBAAAEEEEiOwE477WRHHXWUq7xBgwbJOQi1IoAAAgggECNQt27dyPtPw4YNY9byFAEEEEAgkwQIgGbS1aKtCCCAAAJpJbBlyxZbvXq1rVu3zipVqmSVK1dOq/aVlMYcdthh1qxZM3c6GJeUq8p5IIAAAokLrF+/3v78808rXbq0ValSxcqVK5d4pYEa2rRpY/vtt59bwvtPAIaHCCCAQAYKEADNwItGkxFAAAEEikdg8+bNNnPmTHvmmWfs7bffti+++MLWrl0baYy+HO2111526KGH2jHHHGMtW7aMrOMBAggggAACCCQm8PPPP9sTTzxhr776qs2bN8+WLl0aqVBB0Hr16lnz5s1dr81evXrZtttuG1nPAwQQQACB7BZgDtDsvv6cPQIIIIBAAQQ2bdpkEyZMsF133dWOPPJIGzt2rM2fPz8q+Klq1Bv0ww8/tBtvvNEOOugga9y4sT366KOmnqIUBBBAAAEEECiawOeff24nnniiaUj6eeed536IDAY/Vat+pFyyZIlNmTLFzjjjDKtdu7YNHDjQvv/++6IdlL0QQAABBEqUAAHQEnU5ORkEEEAAgbAFPvnkExfMPPPMM90Xq8LUv2DBAveFTT1CFy9eXJhd2RYBBBBAAIGsF9APkFdccYXtu+++7gfFf/75p8AmGh5/991325577mk333wzP0YWWI4NEUAAgZIpwBD4knldOSsEEEAAgRAENMzuoosusg0bNuSorVSpUqaECPXr13fzf6r3p4Kc6n0SW9555x03h+Vjjz1mHTt2jF3NcwQQQAABBBCIEfj999+tW7dupvfQeEVzfmramerVq7vgpobHL1y40NasWRO1uaaqGTp0qL3xxhum92HN2U1BAAEEEMg+AQKg2XfNOWMEEEAAgQIIqNfIhRdemGNLfdkaMmSI+1K2ww475Fj/008/2ZNPPul6mwSH3SlJQ9euXe3xxx9384Pm2JEFCCCAAAIIIOAEVqxYYUpApJEUwaIkR6eeeqq7tWjRwiU/Cq7fuHGjvfvuu3bPPfe4YKeGxfvy7LPPurlBX375ZYKgHoV7BBBAIIsEGAKfRRebU0UAAQQQKJjAtGnTcgQ/q1at6r5QaUj8aaedZvGCn6q9Tp06dv7559uXX35pN9xwg5UvXz5yUA3l0xxm77//fmQZDxBAAAEEEEDgPwGNujj66KNzBD87derkenjeddddLsmgkh7FFgVI27Zta5MmTXJJklq3bh21iYKjvXv3Zjh8lApPEEAAgewQyPmukR3nzVkigAACCCAQV0C9Nvv16xe1TsmPPvjgAzv99NOtTJkyUetye6LAp3qQashdrVq1IptpTjJ9+Vq1alVkGQ8QQAABBBBA4P8LXHrppTl+KLzsssvsueeec1PPFNSpSZMm9vrrr7tESMF91BNUc4JSEEAAAQSyS4AAaHZdb84WAQQQQCAfAfXeDAYnFbzUcLk99tgjnz3jr27ZsqX7Aqa5ynxRkFVJHSgIIIAAAggg8J/ARx99ZLfeeut/C/59dNVVV9nIkSNNc28XtqhH6IQJE3IEQYcPH052+MJisj0CCCCQ4QIEQDP8AtJ8BBBAAIHwBGbNmmUa/u6Lvjg9+OCDVrt2bb+oSPfKQDt58uSoffWFLDhHaNRKniCAAAIIIJCFApdffrkFM7336NHDFKxMtIwbN86Cw+GVGElBVQoCCCCAQPYIEADNnmvNmSKAAAII5CMQ2+ukb9++tu++++azV8FWd+7c2fRFzhclarj99tv9U+4RQAABBBDIaoGvvvrKnn/++YhBxYoVbezYsUXq+Rmp5P8e6AdNvecG5w196KGHTJnmKQgggAAC2SFAADQ7rjNniQACCCCQj8CaNWuien9WqlTJBg8enM9ehVs9atSoqC9ysb1CC1db9mz9+OOP29577+1u6jlLQQABBBAoeQJKXBQsZ599tu24447BRQk91g+aJ5xwQqQOJVuaMmVK5Hm8B0899VTk/Wf8+PHxNmEZAggggECGCBAAzZALRTMRQAABBJIr8Pbbb9u6desiB+nSpYttv/32kedhPNA8ooccckikqqVLl7qMtpEFPIgroMRRf/zxh7sFr1HcjVmIAAIIIJCRAq+88kpUuwcMGBD1PIwnsXVqju+8Cu8/eemwDgEEEMgsgbKZ1VxaiwACCCCQTQLKlv7NN9+k5JR/+umnqON8+OGH1rFjR7esbNmyUcPmojYs5JPY4yjQusMOOxSyloJvvnnzZtu0aZPpHA4++GC77bbbCr4zWyKAAAIIlBiBQw891P7++++UnU/w/Sc49DxeA7Zs2WJz586NrNpqq62sT58+kedhPdBx1Ba1TUUZ4Vu0aJFr9b/++mtk3b333msvvvhi5HkqHtStW9emTp2aikNxDAQQQKDECxAALfGXmBNEAAEEMlfg008/LbYekt99953pluzy7bffmm6pKMkMtKai/RwDAQQQQKDoAnPmzIka6VD0mpK/p4anz549O+kHKsxx9ANm7I+YyW7gihUrkn0I6kcAAQSyRoAh8FlzqTlRBBBAAAEEEEAAAQQQQAABBBBAAAEEsk+AAGj2XXPOGAEEEEAAAQQQQAABBBBAAAEEEEAAgawRIACaNZeaE0UAAQQQQAABBBBAAAEEEEAAAQQQQCD7BJgDNPuuOWeMAAIIZIxA9erVrXbt2ilp7+rVq+2vv/6KHKtq1apWvnx591wJE0qVKhVZl8gDHUPH8kXHqVSpkn8a+r0SPijZg84h7Kz2oTeWChFAAAEEkiag99N169Ylrf7YioPvP/m9h2rb5cuXR6ooU6aM1ahRI/I8rAc6zs8//2y6V9F7Y82aNXOtfu3atbZq1Sq3fptttrHKlSvnum0yViTDIBntpE4EEEAgEwQIgGbCVaKNCCCAQJYKvPHGGyk78+nTp1v37t0jx+vWrZuNHj3aPa9W7f+xdxZwcxPpHx+cAqVoWyiFluJapC2UK04LRQ5tKe4Oh7tT3L243GGHc8UOh+JW3ClWvLhegfznN38mZLPZ3exudt+V73w+75tkZjLynUySffLM88xgOnXqFKZVs7P66qubu+++OyzigQceMEsssUR4nPWOHDzIiYIcIE0xxRRZF095EIAABCDQJATeeeedura03OfPUkstZZ577jnXxt9//908+eSTpmfPnpm2+amnnjIDBgwIyxw6dKjzBB9GxHbk+X377bd3sXvvvbc56qijYjk4hAAEIACBZiHAEvhmGSnaCQEIQAACNSWwwgor5AgIJRCNaoRmUfmHH35o7rvvvrAoCSUXX3zx8JgdCEAAAhCAQLsSWG211XK6ftlll+UcZ3EQL3PVVVfNoljKgAAEIACBJiCAALQJBokmQgACEIBA7QlIy3ONNdYIK9KSt/PPPz88zmLn8MMPN7/99ltY1PDhw93yuzCCHQhAAAIQgECbEhgxYkROz0877TS3giEnsoqDd99911xyySVhCVpmP2zYsPCYHQhAAAIQaG0CCEBbe3zpHQQgAAEIlEFgjz32yMk9atQoM27cuJy4Sg+eeOIJc+WVV4any+7YbrvtFh6zU5iAlkUedthh7m/QoEGFM5ICAQhAAAJNS0ArIpZffvmw/foQedBBB4XH1ezI5ueee+5pJk6cGBaz4YYblrQzLhM1/vmz4oorhueyAwEIQAACzUcAG6A1HjM9uOXggtAeBGRYXk5TJNhIG+Sc5KOPPjJzzjln3imynSQbSFnZHsyrgAgIQCCHwCqrrGK0FP6hhx5y8ZrTW265pRkzZkxV83D8+PFmvfXWc86IfIWbbrqpWWCBBfwh2yIEFl54YTPHHHO4HPV2QFGkWSRBAAIQgEDGBI455hj3HPbFXnTRRUYfwXbccUcfVdH2yCOPzLH1KZvYRxxxRMmyFlxwQTP77LO7fDx/SuIiAwQgAIGGJtCSAlB92dtuu+2MNHc6UnB0xRVXGBnO1tfFf/zjH6kvhF9//dWcccYZRttokFBtxhlnNDvttJPRko1oeOONN8zNN99sJDDzQYI4fen0Xox9fD23X375pbn00kvNL7/8klitPB/LuLmEf/rTC0YpL5GJBXVgpDxWyki7tLtkWP2WW24p6SHyxx9/dHkfe+wxd970009vrrrqqpxeyEv0Jpts4r5UX3755aZ79+456RxAAAK1IXDeeee5H1v+vvX222+btdde29x2220VzcO33nrLnR/1bitv7CeffHJtOkCpEIAABCAAgSYlIA3Qrbbayujd1we/WqJSIejIkSONBKvRsO+++xoJNwkQgAAEINA+BNKrqTURk/vvv9/Ixsudd97Zoa3Wj2YFv03bGAksd999dzPTTDOZe+65J/yTke5dd901T/ipcueff36z3377GTnY0Dla5qGXhY4UfqpdcvChdmlJi++LHIDMN998Lm7qqad2gkAtLdl4443N/vvvb77++mud2jTh+eefd9pi0hCTADqNAFeCEI3Ra6+9Zr755pvEvkp4rDQJSz/55JPEPERCAALZE1hooYXMWWedlVPw008/bfr165fjvT0nQ8KB5vi1117rvM3qI5UP+pilpfDdunXzUWwhAAEIQAACEPiTwNlnn52zQkK2s6UAssMOO5ivvvoqNaePP/7YbLTRRm4Ju57JPgwcOBBv7h4GWwhAAAJtRKAlBaA33nijG8IbbrjBCZlqPZ7vvfdeYhXS+pQgsxLbNdKM3GWXXUyvXr3CsiUQLRakFdq/f38z3XTTOaHjtNNOWyx73dIkEPz73/9uJp/8/xWOe/fu7YSdipOQ9tRTT3XaurPOOqvThtRy0w8++KBu7au2IjlN2X777cNioi9YYWRsp0+fPmbllVc266yzjktJOkecjj32WCOnKX379o2VwCEEIFBLAprTe++9d04VMlWx+uqrG32Muvvuu3OcGUUzSnv/pptucvdjOXSIf9Q555xzzNChQ6OnsA8BCEAAAhCAwJ8E9FvmrrvuMj169MhhouXwc889t9Pm1DO5UJDyyQEHHGDmmWceo9+D0SCtT63o0BJ4AgQgAAEItBeBllsC/+qrr5o333zTjaK0IR9//HGjr3y1Clr2rB/CSTZkpP1YjWdBaQltscUW5uijj3bNHz16dN4P8ni/tBRbQjUJUBst6GVGGo1eEBptn15QJCyWsE/Cgnvvvddss8020SwNvR8VNqfRAPWd8ecVOidqCN6fwxYCEKgPAd3X9QPp9Z19VwAAQABJREFUxBNPzKlQWuz6ky0waYXqQ5XuuT/88IPT+H/22WeNbIfGg8q64IILzNZbbx1P4hgCEIAABCAAgQiBueaayzz66KPuw+Prr78epsi/gn4v6E92tBdZZBEjJQopE3z22WfmxRdfNO+8806YP7ojRZHbb7/dyAwNAQIQgAAE2o9Ay2mASutGQseuXbu60fz3v/9ds1HVMmbZk0nS3suqUmkJ+mWS+hKqH9iFwvvvv2+0zFK26hoxFBLy+bZGtRwluCZAAAIQ6GgC0uSXfd7ZZpstrymy0yuTK7JzLK3Oy629MpnCSBJ+zjvvvC4N4WceRiIgAAEIQAACiQQkBJUJGq0OSwoSjErD8/zzz3eryeQPIUn4KaUS+UV45JFHnHmupLKIgwAEIACB1ifQUgJQaQ4+8MADZv311zfrrruuGz1p4iQ9CAsNbdRJhfJoebt+5MaD8u21117mu+++c0kSghYShMqWY6VBy9plu0ZBP6q1ZKNQkM3TJZZYIvSUm5RP7X355ZfdF1XZxWmkIG/nPsjuZakge0Aan59++qloVvU5KpDQ1+FXXnnFOReKnqj6Va//izqh0r6P1zaaFi0juq88cn5SzfirPDn18tdZtPzovtr00ksvGQnBxaVQkMd5MZOAWfmjXJLOSTsfks4lDgKtQmCllVYyY8eONQcffLCRw7Jygj7GSYNU802aJwQIQAACEIAABNIT0AoyfWB88MEHzaBBg9Kf+GdOmaqSEPX00083U045ZdnncwIEIAABCLQOgZZaAi/h4NJLL+289K611lrmsssuc8IjaYEWs8MpYdhDDz3k/rSMUefJcYVsiUoAJCHkBhts4JZoa+ilhSm7Ml44pHPXXHNNd1VoubraIAPdelBrKbcEYNVookqjU22SkEtfOaXhGl9GLuGdNETlJCkpSIimr6MSfGmJvF4m5IRDwt3TTjstx7Pxrbfe6paHeMGxvLTL8LiWmKgM1SNv9IMHDzbbbrttolOmpDaUinvuuefCLHKSVChIcCvvyWq7hN6ff/65WXTRRY28OcoukILGSJpY0s6SZ/Zzzz3XyFaQxtU7pZKDKJ0jm34KWlKjL8dXXHGFE2Yr/pBDDnFpEl5IwPzf//7XHev60jWQFDQWslGkuuQUSUFLZI888kgj259pggSVL7zwghPo6xraY489wnZGz5dQRktqZ599djPDDDO4a05fuWU/VkKbaNCSINlblbkBLdtVn6TdtpX1tClTCz6UOx/8eWwh0MoEtNxdNnkPPPBA92zQ80b3mC+++CKv25qPK6ywgllvvfXc/bajndHlNZAICEAAAhCAQJMR0HP14Ycfdh8kr7nmGudcVe+y8Q//EnJKIUS/UzbddFPnKLbJukpzIQABCECgRgRaRgCqh98tt9wSCqUkoNPycdnnlABJAjzFxYMEVLIbKmGlPG3POeecZuTIkU6oJyGSfuDqfAkwJQCUUEnCQwnJjjvuOCcUk0MMLyhT+XLgI1ucElZKWJe0dDLejmLHEspKCCqBmn5sS6inh3o06Mum+qKXg6RwxhlnOO1Rtdl/PV1xxRVdudJkvfrqq0Pv5XJOJHYSjEm4OMccc5hlllnGFStBo/omgaJfmp9UX7lxErqNGjXKnSa7mPLymBQkxFMfjjrqqPCFRoIICUTltERLUf1ymccee8wJfFWOvhyLj4S/v/zyi5GQd/z48U6goaX5Q4YMMXIyJYGutIb1QhUNEmrrT0JhLxiOpkf3xVpCVgnNpW0qO7HSutx5553Neeed5wSQ0fxJ+/fcc4/TUpVAtlDQNalrTH1XnxU0brrWZbtQHL3GmezhStirsTzssMPCIiUYl7C2e/fu7pqqZD6EhSXsSOAq3klhwoQJRsJiCZ6zCuqPhMdZlplV29KWoz74IEG+BNrNGPwPkiQN+mbpj+aDD9I098cSbOpPQR+7NM91neterbnUpUsXf5qLLzQHwkw13tGcUGiF60n9EE/dO5ox+JUi0eup2foRZa850RH3W3+vr5Sd+pD1s0Jt0vh2BI9KOcTP4/kTJ9Jxx/55oxY08/2iFs8fOQnVigz96ZrV+7zmnd7n9fzVu66UV3yodk769xmVx/PHU+2YbaM8f/yzvGMoUCsEIFANgZYRgMqmi774DRgwIOShpfASgOolQsLRJNtrOkdLzCVkkOBKD1FpxXnBkQSe+tEoYZo0CeNadaosbttSQlT96eGr5RZZhA033NBcf/317kffddddlycAveOOO5wQr9DSDglxFSS89UEaTRL6STNy3Lhxofak0pUmrVkJR6XhqmXz0q6UJqoEaNUIPzUeEhDKIZKEzhJqqn16wVhyySUdf2lQxYNecqSBJRbzzz9/mCyhpgS4GjsJA2WLT+Mkm6IyiaCgF6JDDz00PEcCZY2zHGZdeOGFLr9npxeruADUn6i0UgJQGWSPOsWSfSI5eNJycwmO01wTEsjqT+0Xp3iQrVf1U4JgL/xUnoUXXthpmaqN0g7117GMyKt+73TJl+fP1Rd1CdWzmA++bG0ltC+0fF/joxfjUiYMouWl3a9FmWnrzjJfRwvOsuhLq4yF7lvRH6SezdRTT50zBxXfqH1uhetJfPUsiApqFNdsodD11Gz90I/Rjrje9b5QzQ9Qncvzp/jV1gr3i464NotTrSy1Fe4XtbyeZGrG+34Q4TRmqiobCZ4/lXKrxXkd+fzxgv1a9IsyIQCB2hJoTtWiBCbShJPdz6gwcqGFFgoFZRKAFvvB5O266cesFxr5aqT5qSD7ikkhWmc0PSpsjMZXsi+Boxe+Smj3/PPPh8VIw0qaqsWcH2233XZGHsWlwRkNXugnbbx4kMajlnorSLAowZ20RhdffPF41rKOJXCV4E7eG0866SS3pF7akhLwnnnmmTmC2GjBGkNp1GpJvtfE9FsJJhUkqPXjHNX4lf2faNDYeHMB0nKVEDarIA3aaJBAdMSIES7qmWeecRrC0fRi+3GBpc8rswUS6mo84kFL7XfccUcnKPZpGntp/np7sj6+0PhXMx982WwhAAEIZEVAH1JkU1l/0rolQAACEIAABOpBgOdPPShTBwQgAIH6EGgJDVBpE0rL7vjjj8+jJsGalkzrB5O0DOOCsLwTEiL8csaO/pK88cYbuz6oidKsk30bBS2Xlodhb//SRcb+SfAVFX6Jl7RG/Q/JJM0mFbHbbru5Je/vvvuuEyxq6X+1Qe28+OKLncd6LQuXwFIaiJtvvnnRoqWBK01d2eKMBwnytPxbQS8qM888c06WJCG1tE07derkNCOzdAjlhYrRBki7UtqzCjKRIA3hSoPsnspsg7Q9k/ole6P6iwZp1Goe+KBrWdeNrgGFQuPv80e35cyHs846K882ky9LAm8tzZcWclZB/dKX/6jwO6uy61WONM79eEgQHbf3W692VFuPtD00HrJN26zL+NV+r7WijyZJc7taTvU4X/dYfShr5utJ5kD8Ryt95JEt1mYM0ljRckx93GpW27D+ehJ/9aHQh7pajo/qrea+ohU6+sv6+aP7RZZl1pJhUtk8f5KodExc9PnTCveLZn7+/Oc//3GKBboStJosuqKsY66OymptteeP3smyVDZKS7Xa50/aesgHAQjUhkBLCEDlrEjLmWT7MB6idltkM7ESAWi8zPhxkhAqnid6PHr0aLdkOxrn9+VkyAuYfJzfatm3lg1rabOW5EsjVUuYJRCMax36c6JbMdJydtm/lHbslltu6Zb1a+l4oaCXrr333tsth5edO28ntVD+cuLVnz333NNpl6pcLa2XZmMhgY/6qyUHsmWpJfpZBNlnlXBXQsVaBtkj8sJWaZxWE2RPVEHllRvkkOuf//yns+0q0wEyEyFhaq3CsssuW7Do22+/3f2AzVII4JeJZVlmwQ7UKEE/enzQy90UU0zhD5tqqxdthWZ+UYwuo9N9qVmvK/+MaubrKSrs0n6zjoWfF5rXzdoHfz1pfnfUWKjeaDvUlnKCztVflmOg50/WZZbTpyzy8vzJgmI2ZfgPoSqtFe4XPH+yuS6qKaXVnj/6iJXlPTwt22qfP2nrIR8EIFAbAk0vAJW2nzxzyxB21P5LFJcEpHIcJE1RLR33mpPRPNXsl/sSrhu2XgQqCdIClQBUQQJd2cOUYHCVVVYpWpw47b///s6eqZazl+OYScvK1V69jEnLVnYsdfPPIkgIp/IlxNXYnH322c7uaFLZ0kxQkAaltB+zCF7YmmRzNIvyo2XMMsssTuOxGvupKs9r7Up7spwggaPMGMgWrmy7Kniv9uWUQ14IFCIgZ2bexq0+PumeIUF9uffIQuVH4/UhROYlCBCAAATalYDei/QhOaugjz5eSCKTUNF3Pa0ikm1yAgQgAAEIQAACEGhWAk0vANWyBNl/lCf2QkFajBKAKkho2NECUGmhVqqJOnDgQGcDU8Kvu+66yy33XW211YpqA+qFVi/Isp12zTXXlCX8lG1MOeKRZuouu+zihJWytzps2LBCuMuO32effVzbJKC+6aab3HJ+b3s0Wpi0KF977TXnpb2YAFQarT169IieWnDfa2NWsyS9YOGxBO8Nu5ipgtgpiYfioCCNXDGbZ555EvPJ6ZHXEpX5hxNOOME5Otp0000T8xMJgWoJyL7vJZdcUm0xqc7fZJNNEICmIkUmCECgVQnoOV+ve65W7SAAbdUriX5BAAIQgAAE2oNANmp8HcRKGkayI5gkLIs2qU+fPqHQU96wiy35jp6Xdt9/LU+bv5p80qQaPny4K0LaVfJyX6r/L7zwghMwSgPRC8/StEEv1vK6LgHlfPPNF9q/kdf0LBlq+cLIkSNDOy6nnnpqohd2tUFBQlwvTIz3Q9q+Tz/9dDw68VgCRHlY1/Xhy45mVP+zClpir7pkeqB79+5VFSthrbSIFS666KJET7jSEpWdVR8ktFaQCQUCBCAAAQhAAAIQgAAEIAABCEAAAhBoJwJNLQCV8E+CnlLLvzWgQ4cOdeMqO5jXXXdd3hgrvlCI2hGN5vGOdr744oswWjYqffDn+a2Pr3a7+uqrh3ZCJbxbcMEFixYpzU8F2X+MejuXYNQLMr2jj2hB55xzjvMa379/fxcth1IS4EmjVNqExZhFy/H73rZUUl3S2PRGxcVL+1GuKkPL/7UcS0u+tJw/mq5zdD3IU7wfa1+vtn75fDTOCwW33377nCW63pSCuEWF259++mloL1MsywnSVFbbJUyOLgf25Rdi6dOj15C0Or0QXLZgJTyO2iqUiYCjjjoq9Dyvdvpr4L777stptncoFR+TQu3RydG25BTGAQQgAAEIQAACEIAABCAAAQhAAAIQaEACTSsAlUadNNwkDPLacMX4apm8D3JCFLef6IVpP/74oxMU+rzaykGPgs6JCpp69uzp4mW7Uh615WBI2pE++DLl8TV6nk+vdCuNyXXXXdedLhuapULUTp4El3KCo62EhYsssog7XZqTEgi+8cYb7lgmA7T0XV7gfZAAT8JCBdkhjWoY+jyFthK0egYSJCZpcC633HJms802c0VIsL3ffvuZCRMmhEVKe3XEiBHuWHZDtZRbAsUjjjjCrL322kYex2WjNMm+qmyLahx8UN+kPSwbqqo3GpZaail3KM/wsi0r25knnXSS86LunfpozCUg9kvop5lmmtBWllhGwxNPPGGuvfbaUJM2mqZ+KEg71As7fbo0fL1zJl+PT5MdT39Ny46nTCrICdg222zj/qQVLI1fH/w18OyzzzpGV155pWvPvPPO67JofK666irXTkX4a7ec+eDrYgsBCEAAAhCAAAQgAAEIQAACEIAABBqJwGRH2tBIDUrTFgkw5QBDjn0mTpzohHFyZlPIFqKEkrL96YVp0tKUtqCWOC+55JLOqY9sT0rgpPDmm2+a6aef3jlVkldy1SfhlP4kIJTndQmXtBT5ySefdMKihx9+2C3hlsBQdhmlZXrzzTe7MnWebFdKq65QG9P0O5qnV69eTjB3wAEHlPSAJwc/soMq7+ES5kr4KK1OCcxmmGEG5wlegjbZplxppZXMeeed5wS5MoAvzUxpmSpIgCmNQa9FKg1SCTNlF0rlJwVpSkr4KBuiXstQPGSTUs6bdK6Ehz7IPutLL73k2qk2yc6phH/LLLOMy7L00ku7/BIcavwkuBs3bpxRvISUcedOl112mTtPNkvVhgcffNBdC9puueWWZrvttvNVh1tpgHbp0sU5ZRIz1SXeYq261CcJnsVK14A0OtWHxRZbzAks1bc77rjDPPPMM06gqLGXRqYXnqoiXUdyJqV2iIeuPV13ujZ1jUg4q7Z7Qf0777zjtFjVP12but6l6ao5IC/2mgcSWnbu3Nkcc8wxOXWpPrVNYyVeEuxON910bvylPa3xVLyEndIslSC0kvmgesoJ4iRBt9dmLefcQnnFUSzUv2YNmidey1bXVZoPPI3SV12n//rXv+rSHM1f/xGgVhXqnqfrSUH3Q3nibcage4zul812PUVZ636qj1EKuveusMIK0eSm2dd7gO61rXA9CbrmhPpS76APf3r+yTFPJUEfefVu4T+qVlJG/JyOeP5oZY0cW9YjyN58/INxPeqtpA7dt3X/1rtAdNVNJWV11Dm6nvSn0Ar3i2Z+/jz33HNGK7kU9OzRM6gZA8+fbEZNSk/6jZqlP4xsWkYpEIBAGgKT2Jth4bXfaUpo8zzCJ0GelsNL+FTPIGFcOQIA/QiWpqFfuu/bKoGLBGreI7qPb+StBIXqv4S5EtAWYj9o0CDXjTPPPNMt59fycP1gS+P0SD8sJCwUYy+E0lL6NMI1naeX76i9zlrxlLBM1+BMM81kZpxxxqLV6IEtVvGxFsdCDIsWWEXigQce6JbmxzVmqyjSXQ8at27dulVTTIeeqzHyHwv0oaWZhG76sCQTHfUIMuew8sor17QqCVgkrFLQxyLvVKymldagcP2I1gfAZrueoihGjRpldt55Zxd19NFHm8MOOyya3DT7eg5L8NYK15Oga06oL/UOWv0hp2tJJo3StOWQQw5xHzdvvfXWNNlT5dFzVPeLau18p6rsz0z68OnN9pRzXiV59ZFZK3OaIeg9QKt+9C4Q9WTfDG33bdT15M03tcL9opmfP1rx5lfAHX744U6pwY9TM215/mQzWjLDJuUTb0otm1IpBQIQqBeByetVUavWoy/Lvax2YEeEcoSfap+EeHHhp+I7QntD9VYT9ELrNVPTliNBUjnn6Gt1XGM3jfBT7ZHWbb2ChJlp+1VIQFpv4We92FAPBCAAAQhAAAIQgAAEIAABCEAAAhBoWhugDB0EIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgVIE0AAtRYj0piWgJeg+eDt+/pgtBCAAAQg0DwE5evM22GQ7mgABCEAAAhCoB4HBgwfz/KkHaOqAAAQgUAcCCEDrAJkqOobAK6+8ElYsx1QDBgwIj9mBQDsTkBMub2c0Sw6yiVSvIIdLSSY9qq1fNg3nm2++aovh/IwJzDrrrGappZZypWKyI2O4FFcxAdlslm1xBdn/lLOxzz77rOLyyj1RjgTrFWTbXM4vOypo3stZJwEC9SYg+6U8f+pNnfogAAEI1IYAAtDacKXUDiYgxwhjxowx8847r2uJPL7KYYI8N/bv37+DW0f1EOhYAuuvv7559dVXO7YRVda+ww47VFlC8umLL764GTt2bHIisRCAAAQiBEaPHm222GKLSEzr7p5xxhlGfx0V5PROzu8IEIAABCAAAQhAoFICCEArJcd5DU1g+PDhRn8ECEAAAhCAAAQgAAEIQAACEIAABCAAgfYmgBOk9h5/eg8BCEAAAhCAAAQgAAEIQAACEIAABCAAgZYmgAC0pYeXzkEAAhCAAAQgAAEIQAACEIAABCAAAQhAoL0JIABt7/Gn9xCAAAQgAAEIQAACEIAABCAAAQhAAAIQaGkC2ABt6eGlcxCAAATyCeyyyy7m888/z0+oMubtt982V199dZWlpDt98803N717906XuYxc3bt3LyM3WSEAgXYmsNhii5nDDz/cIfj111/NxIkTzXTTTVc3JPI8f+qpp9alvlVXXdUMHDiwLnUlVYIH+CQqxEEAAhCAAAQgUA4BBKDl0CIvBCAAgRYgsOuuu9akF3fffXfdBKBbbbWVkVdgAgQgAIGOIrD44osb/Sl8//33RgLJen5E+eKLL+omAB08eLDZb7/9Ogo19UIAAhCAAAQgAIGqCbAEvmqEFAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQg0KgEEoI06MrQLAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQqJoAS+CrRkgBEIAABCAAAQjUksCbb75p7rnnHlfF3/72N7PsssvWsjrKhgAEIAABCDgCsm8uEz8KsoO73HLLuX3+QQACEIBA8xFAANp8Y0aLIQABCEAAAm1F4IknnjD777+/6/PRRx+NALStRp/OQgACEOg4Ak899VT4/JHTMwSgHTcW1AwBCECgWgIsga+WIOdDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACDUsAAWjDDg0NgwAEIAABCEAAAhCAAAQgAAEIQAACEIAABKolgAC0WoKcDwEIQAACEIAABCAAAQhAAAIQgAAEIAABCDQsAQSgDTs0NAwCEIAABCAAAQhAAAIQgAAEIAABCEAAAhColgBOkKolyPkQgAAEIOAITDnllGammWZy+0EQGP1NMskk7i9rRFNMMUXWRVIeBCAAgaYiMOmkk4b33Cwa/scff4TFxO/dnTp1CtPYgQAEIAABCEAAAs1IAAFoM44abYYABCDQgARWWmklM2HCBNeyn376yXz77bemW7duRj/SCRCAAAQgkC2BmWeeObznZlHy119/bX755RdX1CyzzGL40JQFVcqAAAQgAAEIQKBRCPCrtFFGgnZAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACmRNAAJo5UgqEAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEGoUAAtBGGQnaAQEIQAACEIAABCAAAQhAAAIQgAAEIAABCGROABugmSOlQAhAAAIQgAAEsiQgO7KTTTaZKxKbslmSpSwIQAACEChGQA7BeP4UI0QaBCAAgeYhgAC0ecaKlkIAAhCAAATaksBmm21m1l57bdf3zp07tyUDOg0BCEAAAvUnMGLECDN06FBXMc+f+vOnRghAAAJZEmAJfJY0KQsCEIAABCAAAQhAAAIQgAAEIAABCEAAAhBoKAIIQBtqOGgMBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgkCUBBKBZ0qQsCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoKEIIABtqOGgMRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgECWBBCAZkmTsiAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIGGIoAAtKGGg8ZAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACWRJAAJolTcqCAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEGooAAtCGGg4aAwEIQAACEIAABCAAAQhAAAIQgAAEIAABCGRJAAFoljQpCwIQgAAEIAABCEAAAhCAAAQgAAEIQAACEGgoAghAG2o4aAwEIAABCEAAAhCAAAQgAAEIQAACEIAABCCQJYHJsyyMsiAAAQhAAAIQgEDWBMaMGWMuuOACV+yIESPMhhtumHUVlAcBCEAAAhDII/D444+b8847z8UPGzbMDB8+PC8PERCAAAQg0BwEEIA2xzjRSghAAAIQgEDbEnj33XfNTTfd5Prft2/ftuVAxyEAAQhAoL4Exo0bFz5/FllkkfpWTm0QgAAEIJApAZbAZ4qTwiAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIFGIoAAtJFGg7ZAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACmRJAAJopTgqDAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEGokAAtBGGg3aAgEIQAACEIAABCAAAQhAAAIQgAAEIAABCGRKAAFopjgpDAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEGgkAghAG2k0aAsEIAABCEAAAhCAAAQgAAEIQAACEIAABCCQKQEEoJnipDAIQAACEIAABCAAAQhAAAIQgAAEIAABCECgkQggAG2k0aAtEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQKYEEIBmipPCIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgUYiMHkjNYa2QAACEIAABCAAgTiB7t27m4EDB7ronj17xpM5hgAEIAABCNSEQLdu3cLnz5xzzlmTOigUAhCAAATqQwABaH04UwsEIAABCEAAAhUSGDx4sBkwYIA7u3PnzhWWwmkQgAAEIACB8gisssoqZumll3Yn8fwpjx25IQABCDQaAZbAN9qI0B4IQAACEIAABCAAAQhAAAIQgAAEIAABCEAgMwIIQDNDSUEQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAoxFAANpoI0J7IAABCEAAAhCAAAQgAAEIQAACEIAABCAAgcwIIADNDCUFQQACEIAABCAAAQhAAAIQgAAEIAABCEAAAo1GAAFoo40I7YEABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQyI4AANDOUFAQBCEAAAhCAAAQgAAEIQAACEIAABCAAAQg0GgEEoI02IrQHAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQyIwAAtDMUFIQBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0GgEEIA22ojQHghAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCAzAghAM0NJQRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgECjEUAA2mgjQnsgAAEIQAACEMghcNNNN5l+/fq5v4svvjgnjQMIQAACEIBArQjceuut4fPnwgsvrFU1lAsBCEAAAnUgMHkd6qAKCEAAAhCAAAQgUDGBH374wYwfP96d//3331dcDidCAAIQgAAEyiHw448/hs+fb7/9tpxTyQsBCEAAAg1GAA3QBhsQmgMBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhkRwABaHYsKQkCEIAABCAAAQhAAAIQgAAEIAABCEAAAhBoMAIIQBtsQGgOBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgkB0BBKDZsaQkCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoMEIIABtsAGhORCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEB2BBCAZseSkiAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEGI4AAtMEGhOZAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAC2RFAAJodS0qCAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEGozA5A3WHpoDAQi0EYHXXnvNLLPMMpn1OAgCo79JJ23ebzu+D4LSzP1Q+//444+m7gNjoVFsjPDLL7+Yueee2zXm+uuvN7fffntjNKyCVjT7vFCX1QeFSSaZxP25gzr+++GHH8zCCy9cVY1vvfUWz58YQe55MSAdeMhYdCD8WNXR58/NN99s7r777liO5jnk+VP9WOn5s8ACC1RfECVAAAIdQgABaIdgp1IIQGDVVVc1PXr0yBSEXuz0o2GyySbLtNx6Fvbmm2+ar776ylW56KKLmmmnnbae1WdWl8ZCfxoLCUmaMXzwwQfm448/dk2fd955zcwzz9yM3XBz4vfff2/qsXjvvffM66+/7vhLEDrffPMxFh1E4LvvvjOvvvqqq71r166hYLrezenevXvFVa688sqmmvOTKm6F54+EwhMmTHDdW2SRRcx0002X1NWGj2uF58+HH35oxo8f71jPM888ZpZZZml47kkN1DtZsz9/9C7g73m9e/c2888/f1JXGz7Oj4U+rjfrB/bvv//evPLKK471rLPOavr06dMh3Lt169Yh9VIpBCBQPYFJ7M0wqL4YSoAABCAAgSwI7LXXXuaOO+5wRd166618Zc4CaoVlnHbaaeaCCy5wZ5911llmyJAhFZbEadUSuPHGG83BBx/sitl3333N9ttvX22RnF8hgWeeecZsuumm7uyNNtrIjBw5ssKSOK3RCOyzzz5m9OjRrlmacxKCEjqGgJ455557rqv89NNPN0OHDu2YhlCrueWWW8wBBxzgSOgdbaeddoJKBxF4/vnnzcYbb+xqX3/99c3xxx/fQS2hWghAoFkJNO860WYlTrshAAEIQAACEIAABCAAAQhAAAIQgAAEIACBuhFAAFo31FQEAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEI1JsAAtB6E6c+CEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoG4EEIDWDTUVQQACEIAABCAAAQhAAAIQgAAEIAABCEAAAvUmgAC03sSpDwIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKgbgcnrVhMVQQACEIBASQI9evQIPb9PNdVUJfOToXYEunbtGo5F586da1cRJZckMMMMM4RjMcsss5TMT4baEZhmmmnCsZhtttlqVxEl151A9PnTqVOnutdPhX8RmHXWWcN51qVLl78S2Ks7gejzZ+aZZ657/VT4FwHdlxZYYAEXMfvss/+VwB4EIACBlAQmCWxImZdsEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAASaigBL4JtquGgsBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUA4BBKDl0CIvBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0FQEEIA21XDRWAhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKAcAghAy6FFXghAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKCpCCAAbarhorEQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAOQQmLyczeSEAAQhAoDoCf/zxh7n//vvNnXfeaYIgMJ07dzaLLrqoWX755U3Xrl2rK/zPs9955x1zwAEHmF133dWstNJKmZTZioXUYiweeOABc9ttt5l3333XdOrUyfTp08dsttlmZsEFF2xFhBX36ZNPPjHXXXed0bU6zTTTmNlnn90ss8wypl+/fmbSScv/Npt1eRV3rAlPZB404aBV2ORajHW8KTx/4kSSj2sxFjx/klnHY7N+XmRdXry9rXzMPGjl0aVvEGhMApPYH+BBYzaNVkEAAhBoLQITJ040Rx99tHn66afNiSeeaBZffHHz22+/mTPOOMM89NBD5qKLLjLdu3evqtO//vqr2X777c24cePMIYccYlZfffWqymvVk7MeC73Ei/eYMWPykEmgt8cee5gNNtggL60dI55//nlz4IEHmoEDB5qDDjrITDnllOaNN94w++67r1lzzTXNTjvtVBaWrMsrq/Imz8w8aPIBLKP5WY91UtU8f5Ko5MdlPRY8f/IZF4rJ+nmRdXmF2t2K8cyDVhxV+gSBxidQvppF4/eJFkIAAhBoSAKXXHKJefDBB822227rhJ9q5OSTT2723HNPM/3005v999/f/PTTT1W1fdSoUeaDDz6oqox2ODnrsbjwwgud1udJJ51k7rjjDjN69Ggn5JOGr36cnnPOOU4o3Q5si/Xxq6++MgcffLDT+tT1LuGnwvzzz2922203c9VVV5nbb7+9WBE5aVmXl1N4GxwwD9pgkP/sYtZjnUSO508Slfy4rMeC508+46SYrJ8XWZeX1OZWjmMetPLo0jcINC4BBKCNOza0DAIQaCECn332mbn22mudwFNabtEgIajipLWpH5CVhieeeMK89dZbpm/fvpUW0RbnZT0W33//vTNrcPHFF5tll13WmTXo0qWLG9Njjz3WMZWmbzmCvVYdCP3g+eGHH5xmskwERMMqq6xipp12WnPyySebL7/8MppUcD/r8gpW1IIJzIMWHNQCXcp6rJOq4fmTRCU/Luux4PmTz7hQTNbPi6zLK9TuVoxnHrTiqNInCDQHAQSgzTFOtBICEGhyArL7+fvvvzt7n7J5GA/9+/d3Uffee6/RsqByw9dff23OOussc+ihh1ZkQ7Hc+po5f9Zj8fDDDzs7n9L2jIclllgi1PaVXdB2DhICi73CgAED8lDoQ8CSSy7p5onmQamQdXml6mu1dOZBq41o4f5kPdbxmnj+xIkUPs56LHj+FGYdTcn6eZF1edG2tsM+86AdRpk+QqAxCSAAbcxxoVUQgECLEdDLnkKvXr3cNv6vZ8+eLkraHI8//ng8ueTxCSecYLbaaquqbYiWrKgFMmQ9FnLgM3To0IJk5pprLpc23XTTFczTDgmyfSvtTwXPJN7vOeec00Xddddd8aS846zLy6ugxSOYBy0+wJHuZT3WkaLdLs+fOJHCx1mPBc+fwqyjKVk/L7IuL9rWdthnHrTDKNNHCDQmAQSgjTkutAoCEGghAtIUeP31112P9GMlKUw11VRm5plndkmvvvpqUpaCcTfffLOzqTh48OCCeUj4fwK1GAtpeUp7sVD45ZdfXNLcc89dKEtbxL/88suun1r6PuOMMyb22c8PeZKWQ5ViIevyitXVamnMg1Yb0cL9qcVYR2vj+ROlUXy/FmPB86c4c5+a9fMi6/J8O9thyzxoh1GmjxBoXAIIQBt3bGgZBCDQIgS0PNCHYlqAsn+o8MUXX/jsJbfvvfeeueGGG8w+++xTMi8ZjKnlWBTi++abb5opppjCDBkypFCWtoiXwwiFNHNA+UrZAc26PNXZLoF50C4jXdt7Hs+f8q4j5l15vLLMnfXzIuvysuxro5fFPGj0EaJ9EGhtAghAW3t86R0EINAABCZMmBC2QpqehYJPSysAla3QkSNHmv3226+oUKlQfe0YX6uxKMRSjq3ef/99s/7665vZZputULa2iPfsp5566oL99XNAGUrNg6zLK9ioFkzw7NS1KPN4V31aqbGInxc/Zh7EidTvuFZjzfOn/DGs1VgUagnz7i8ynj3Pn7+YdNSeHwvV758xSW3xaTx/kugQBwEIVEoAAWil5DgPAhCAQEoCWu7jw5RTTul387Z+GXU0f16mSMSFF15oll56aby+R5iU2o2yzXIsCtV77rnnGi1932677QplaZt4z17asIWCnwNK9/kL5fXpWZVXqJ5WjPfs1DfmQSuO8F99qtVY8/z5i3HavVqNRaH6ef78Rcazz+p5kXV5f7W09fc8O/WU50/rjzc9hECjEShstKzRWkp7IAABCDQpga5du4Yt/9///hfux3d+/vlnF9WlS5d4Ut7xM888Y8aOHWvOP//8vDQiChOoxVgUqk3eed9++21zwQUXmGJaJ4XOb7V4zz7NHFDfS82DrMtrNd7F+uPZKU+a8Sg1FsXqYh4Uo1P7tFqMNc+fysatFmNRqCXMu1wynn2a+53OLHXPy7q83Na29pFnp16mGY9SY1GMFvOgGB3SINCeBBCAtue402sIQCBDAqNHjzZXX311YokSUMq50WSTTWZ+//1344WcSZl/+uknF+2dISXlUZw8xR9//PFm9913Nx988EFeNl/H559/bt59912XLu/aUe26vJNaJKLeY1EIm7iffvrp5uSTTzbdunUrlK2t4j0Hf30mdd7PAaWVmgdZl5fUnlaNy/qeVIgT86AQmezi633P4/lTeOzqPRaFWsK8yyeT9fMi6/LyW9y6MTx/Wnds6RkEmoEAAtBmGCXaCAEINDQBCTeLLeNR+iyzzGI+++yzHCc88U554c8iiywST8o5/vjjj42Em4cddlhOfPzgoosuMvpTuPXWW81MM80Uz9Jyx/UeiySA33zzjTn88MPd37zzzpuUpS3jvNbHd999Z/744w8z6aT5Vnj8HJA3+FLXa9bltdOgZH1PSmLHPEiikn1cve95PH8Kj2G9xyKpJcy7JCrGZP28yLq85Fa3ZizPn9YcV3oFgWYhgAC0WUaKdkIAAg1LYI011jD6KxaWXXZZc8sttziHOEn55BXzhx9+cElLLrlkUpYwbsYZZzQbbbRReBzfGTNmjPnkk09M//79zVxzzeWS22UJdr3HIs7+119/dYLPnXbaySyxxBLx5LY+7tevn9OElvMUCVHmmGOOPB5eo7nUHNCJWZeX15gWj8jynhRHxTyIE6ndcb3veTx/Co9lvcci3hLmXZzIX8dZPy+yLu+vlrbHHs+f9hhnegmBRiSAALQRR4U2QQACLUdg2LBhTgvz1VdfTezba6+95uLl1MhrFiRmtJFK32OPPQolm/HjxzsB6GqrrWZWX331gvnaNSHLsYgylGH/I444wqyzzjrmb3/7WzQp3H/ggQfMcsstV1RjOMzcYju6bldddVVz9913m1deeSVRAOrnwdChQ0v2PuvySlbYYhmYBy02oEW6k+VY8/wpAjpFUpZjEa2O50+URv5+1s+LrMvLb3FrxzAPWnt86R0EGplA/vqzRm4tbYMABCDQpAR69uzphGISTr788st5vXjiiSdc3BZbbJGXJs3Qyy+/3AmO8hKJKJtANWOhyp599lkzatQo8+mnn4Z1B0FgjjvuODNgwAAzaNAgI02c6J80Hu+55x7z6KOPtqXw04PaeOON3a6EoPHw5ZdfOqdR0pxddNFF48lOQzo+D6opL6+CNouoZh4UuicxDxrzIqrFWDdmTxu/VdWMhXrH86fyMa7meZF0z6umvMp70RpnVjMPksZCVHj+tMa1QS8gUGsCaIDWmjDlQwACEPiTwAEHHGC++OILc9ppp5lzzjnHTDPNNC7ljTfecNqhW2+9deKy6RtvvNFccsklLq9sSs4999wwrZJApWMhoeZBBx3knFl99NFHZuTIka4lcngkAaf+NL6FwoknnlgoqS3i55lnHnPggQeak046yQn0hwwZEvZb3LS89tBDDw3jojtJ86Ca8qJlt+t+pfMgaSzEkHnQuFdS1mPduD1t/JZVOhY8f6ob22qeF0n3vGrKq64nrXF2pfMgaSxEhOdPa1wX9AICtSaAALTWhCkfAhCAwJ8EunTpYs4880xzyimnmN1228307dvXaRG+/fbbZp999nFLp5Ng9ejRw0XLjmcpxzBJ5xOXT6DSsZCzKy19e//998Ml3FdeeaW5+eab8yuJxeg8aYi2e1hzzTVN586dzaWXXmoeeeQRI2+60oDu1auXOffccwuagCg0Dyotr93HQf2vdB4kjQXzoLGvqCzHurF72vitq3QseP5UP7aVPi+S7nlqTaXlVd+T5i+h0nmQNBY8f5r/eqAHEKgXgUmsunhQr8qoBwIQgAAE/p+AvGCPGzfOaYFKAJTkETvK6r333nPacXphJGRLoNyxkBaOxmP++efPtiFtWJo8wsuUgBwieY3oYhhKzYNyyytWV7ullTsPSo1Fu/Frpv4y1o0zWuWOBc+f7Mau3OdFqXteueVl15PmL6nceVBqLJqfCD2AAARqRQABaK3IUi4EIAABCEAAAhCAAAQgAAEIQAACEIAABCDQ4QRwgtThQ0ADIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgVoRQABaK7KUCwEIQAACEIAABCAAAQhAAAIQgAAEIAABCHQ4AQSgHT4ENAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCoFQEEoLUiS7kQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAhxNAANrhQ0ADIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgVoRQABaK7KUCwEIQAACEIAABCAAAQhAAAIQgAAEIAABCHQ4AQSgHT4ENAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCoFQEEoLUiS7kQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAhxNAANrhQ0ADIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgVoRQABaK7KUCwEIQAACEIAABCAAAQhAAAIQgAAEIAABCHQ4AQSgHT4ENAACEIAABCAAgVoQ+PXXX82PP/5Yi6KbrsyJEyeaH374ocParbEYN25c0frT5ClaAIl5BP7444+8OCLyCXz//ff5kcRAAAIQgAAEINBSBCZvqd7QGQhAAAIQgAAEOoTAxx9/bI466qiyBY6TTz65ufzyyzNv89dff20WWGABI6HaCy+8YOaaa67M62j0Aj/99FNz3333mbvuusvcfvvt5tprrzWDBw8u2uxLL73UPPDAAyYIgqL54onDhg0z66yzTjzasT/rrLPMFFNMYW688UZX7qabbmrOOOMMM8kkk7j8Gp9SefIKJsIR+Pnnn83IkSPd+L7xxhumR48epl+/fmbIkCFmkUUWMdtss4158cUXoRUj8Pvvv5vHHnvMcfvPf/7jmNXiPhSrlkMIQAACEIAABDqQwCT2Bbe8N9wObCxVQwACEIAABCDQuAT0SiEh2yabbOIaKeHmySefbGacccaw0b/99pv55ptvzFtvvWX+/e9/u/1aaKm98sorTgCkiiXQW3HFFcM2tMvOPffc4/p+/PHHuy7ff//9ZqWVVirZ/S+//NL8/e9/dwIiZV5ttdXMiBEjzKST/rVwSJq1EyZMcMJVCZIk/D788MNzypZW3ZJLLmkuueQSs/zyy5unnnrKDBo0yPzvf/8zH3zwgenZs6dJkyenUA5CAppHAwYMcCz32GMPIyF0p06djMb5uOOOM5988onL++2335rpp58+PI8dY/Rx4F//+pc57bTTHKctt9yyJh9iYA0BCEAAAhCAQOMQQADaOGNBSyAAAQhAAAItQWDmmWc2X331lenVq1fRZc9PPPGEWXbZZY2WZ0tYWkmQ5um0005runTpknf6LbfcYn766ScnvPPahnmZOjjitddeMwsuuGDNWiHh8jTTTOM0YR9++GEngExT2dlnn20kVFOQZpwERIXCGmusYZZaaimniRjNs+uuu5pRo0a5MZhqqqlc0t13323ee+89s+OOO7rjNHmiZdZiv9ZjUIs2q8xtt93WSGP3pJNOMvvtt19ONd99951ZYYUVzNixY82rr75a02ssp+IGPSh0n9hll13M+eef765vNEAbdPBoFgQgAAEIQCAjAn99ys+oQIqBAAQgAAEIQKC9CXiNz6jGYBKRZZZZxiy22GJOIzApvVSctEmHDx/utLmS8q677rpOG7VRhZ933nlnntAwqR/VxGkMpBWoMNlkk6UuaqaZZgrzlhrHHXbYIXEMJXCV4NMLP1WglmZ74aeO0+RRvlqFeoxBLdouwfZ1113nipaWbTxI4/OOO+4wnTt3NhL+tXModp9AM7adrwz6DgEIQAAC7UYAAWi7jTj9hQAEIAABCNSYQDkCR9nfm3rqqStq0UEHHWTGjBlT0bkdfdL7779vNt98c1OL5f+F+lZKkFnovFLxa665ptlzzz1zssnGoswcFAtp8hQ7v9q0jhiDatvsz5cJAe/gS0LcpDDbbLM57efx48cnJbdNXDPfJ9pmkOgoBCAAAQhAoA4EEIDWATJVQAACEIAABCCQS0C2H1966SUz55xz5tiW9LnksVxL5PXnPTRLk8sH2bU85ZRT3KFsj/o/n+63sjep5fhJQcJHLRX2QWW8++67brm4j/NbOVN65513jIR2xYLK1JJqad/JNqYXUkXPkeBNdjVlQ1OhUNv9OZ9//rmrW/nShA8//NAJhrX8v9bh8ccfdw6OZp999rAqtVOsxEzB9y/a/jR5wgL/3CmHg8p/8803jdrnOUfLK3cMoufG9yVg1HUT7V88jz/W9SbbnT7o2n755Zf9Yept9+7dQ7MRl112WWivNV6ATAxEtXnj6eUe12LOaH5G54mu3yeffDJRq9i3N808U9609wlfrrYqW/emesyfaL3sQwACEIAABCBQWwIIQGvLl9IhAAEIQAACEEggIAc9cpiUFCTQkW1QeTCXAFSOe+add14nVFT+8847z5x44onhqVpKL7uj3sO5BBgPPfSQkfBHWnCjR48O82pHNhGlFSbP8GqDhFDbbbedExT16dPHOW2SbUUFCat23nlnZ2N0nnnmcWlHHHGES4v/kzZq3759nQMaCd3keV3tkkMaH+SQZu211zYSwCncdNNNLo/yqb/RIActKm/o0KFm4YUXNjPMMIPrd1QQHM2vfs4///zOHqTqXmKJJczee+8dCm3L0cyNlltsX8vZo+25+OKLjTh5Z0vyUq5j//fcc8+ZNHmidZbD4ZdffjGy6ygB80UXXeTsY3bt2tWZQvj6669dseWMQbQd0X0J6HfaaSd3Df3jH/8wG264obtG5DzKj63Pr2tB9iU17mqLrk0JiDWus8wyi1l00UVdOa+//ro/peRWWtMbbLCByycBonjL3mo8yMTEWmutFUbLLq7mkswi6E+e4jUe0XDGGWeYbt26mVlnndVcffXVLinrOaMx+Oc//+naJmGuBI5ySqRrVh9FNKdlSkN54iHNPNM5pe4T8XLl/EvXs4T54qb5pvkTDZXyi5bBPgQgAAEIQAACHUTAfq0mQAACEIAABCAAgcwIWGGXVBWDueeeO7FMK3AMrFfw4OCDD85Lt1qTgV2qHdx7771hmhWWBFZQE1iv8WGcdaTj6lA948aNC+O1Y4UmgRWChelXXHFFmP7ggw8GW2yxRWDtYbr07bffPlh11VUDK1ANrIOeQMcqc8oppwysoMq1c//993dpxxxzjGub0m+77bawTO1YbcNguummC6wAJyd+3333deVZ4U5O/FZbbeXirXOhnHgdWC3TwHpdD7bZZpvAChdduhXOBFZQ5s7Zeuut886xQqvAOjsKrGA5TLOC4MAKb905arMVJodppXbUXp2jvyuvvDIxuxgo3WrZ5qVrHJUmzoVCqTzlcvjoo48CK7gKDjjggEB998HaHXVtWW655XyU2xYbg5yMsQO7tD/QNW492gdWQB6mWq1f119dB9a0Qxgv7hpLz9M6l3LnnnrqqYF1wOPmidJ0jZcTrCfzwAq8w3JVxsorrxy88cYbRYt59NFHAysMd+dZIXFiXvsBIjjhhBNcWi3mzPXXXx9stNFGYdutMNi13Qotg5NPPtnx9bw0n30od54Vu0+oTF0rqscKzIMBAwa4+a+x07y0jtlc2jXXXOOrd9ty+eWczAEEIAABCEAAAh1GQMt1CBCAAAQgAAEIQCAzAl4Aap3fBFaTK+dPQh4JiCR0SBKAWm/WLs1qyOW0x2q3BVEhYlSwof2kYDXYXFlRAajPZzUrXVr//v2DL774wkc7wZmEmGqfBLh2aXOYph0vyIoLISXM0jnW83ZOfqtd5+Kttl5OvBe+aRsPl1xySWA1A4OJEyfmJEWFkvfff3+YZrXznOBNQt94sA5wXP1qm11WHE8ueBytS+MZHcd+/foFVns2LLdWAtByOVinV06IKMFpNNx8882urRJo2WXNYVKxMQgzJeysuOKKjrfVWsxLPfroo11duvYktI4Gq+Xr0iSQjbbj2WefDVlKiFtO0McBq3Uanq9x1ryzWseh8DypvPXXX9+dE79elVftnnbaafPan/WcsSYNwnZbe7g5zZRg2Tp3cuk9e/YMrGavSy93npW6T3gBaI8ePfI+pOh6Ek99FImHSvjFy+AYAhCAAAQgAIH6EmAJvH2zIUAAAhCAAAQgkD0B2R486qij8v6sVqSxApbECr29Qi091TJjH4YNG+aWUfvj6LbQ0u4uXbpEs+XsW6GKO1599dXdMmSfqLK0/F7BaviZ3r17+yS31XJlBSuoclv/b7311jPyOq/l0NHgHTx98skn0ehwP952LeE+9NBDzcCBA50tUS0N9n9aLu3DAw884HeN1VB1y9y33XbbMM7vyASA1Qz1hxVt1bekcbTCs4rKS3NSuRys9q7R8mQtYY47e9K4aDn0jTfe6JZ9x+uPj0E8PXqsMqxGpLHCcbd8PJqmfS2/n2KKKYwVqhOvsNUAAEAASURBVJuRI0fmJMvMgYKuOS0/90HLrf0YyX5sOUFezNVvq6VotJRcQbZX7ccFY7WsjdUSTSzOCv5cvJbjP/PMMzl5rKa1sUJaZ5ohmpD1nNESe8/eCqOjVTlzAt7Gr7dpqwyVzjOd6+vSfjwstdRSplevXjnRVtDvjl955ZWceB1Uwi+vECIgAAEIQAACEKgrgcnrWhuVQQACEIAABCDQNgQk5PF2OaOdttpT7lAOiuJh0003NYcccoi59dZbjdUAM1bjy9lOlD1Au0Q1nt0dFxNsJJ5gI4udU0xwKtuICt4xkzuw/yQotZqG/tCly36ibD8qSKCXFOLtkLBTwlLZkTz22GPzThk+fLiLky1JBTlqkb1PBdn/TAp2Ob/LF68rKW9SnIS+SeMoYZQXiiWdV01cuRzscnxX3XzzzZdYrey4FgrlcJHAUEEC0KQgIafsj8oJluzXpglWM9XZm9RYRoX+ac71eTbeeGOzxhprmH322cdYzVkXLSdcElI//PDDzqanz6ut1Xx2aeqP5pgEqD5Y7V9nI9cf+20xTpXMGV+utklly65p586d3VySzdRVVlml4nlWqI5oG+L7VivURUmYHQ+V8IuXwTEEIAABCEAAAvUlgAC0vrypDQIQgAAEIAABS0BC0KgWo4cigZqc3uy1117G2lo066yzjrE2Os1VV13lHMj4fNFtkvAkml7uvtfaTDrP11XIG7xdcu48T0trbYcddnCOmIoJwnx5vi6vAWiXWRs5oykVrL1H531cGoSFtGp9GfG6fHylW2kfbrLJJonCq0rL9OeVy8Fr6ZVi4MuPbsvh4j22e23OaDl+f6GFFnICUDkOqmeQEFIOjTS3NttsMyOnT9ZmptMolkOoeJDmsASgN9xwg3OuJedD8mZvbeo6LdV4/mLH1cyZYuVKK1PCcLtcPidbufNMJ5czzjmVFTjIkl+BKoiGAAQgAAEIQCBDAiyBzxAmRUEAAhCAAAQgkI6AtAqtM5jEzFpGbh2NOE/MymAdIjlt0Keeeioxf9aCjcRKYpFJdcp7vTxszzHHHG5Zsrx8J+WLFhVPl3dshbQewSWsUpD24Hfffef26/nPOvEx0mDMOpTL4bPPPnNNkNC83BAfg2LnW6dOLlkCuEJB468QX4pfKH8l8VqGXyjoutOyeN8vLWlPCsqneWgdbZkzzzzTZdGHBglPazGmvj1JbSkUJ3MCClGN20rmmcqopH6dVyjUm1+hdhAPAQhAAAIQgEA6AghA03EiFwQgAAEIQAACNSIwZswY8+KLL7rSf/zxR7eVHc7nnnvOSMAmrb7x48cb64wksQVZCzYSKykRee211xrZ4JT2nbcPWOIUlxxvu3U45OKffvrpvGX20fIkgPv555+dsNXHey1IfxzfxuuKp1dzbL2umwsuuKCaInLOLZeDBM8K1oN3TjnxA399RePL4WKdeLlT33777WgROfve3MHCCy+cE5/lgezExm13RsuX/U/r9d5FSTAuAXlSsE7HXLQ0RCV0lgA0bo8z6bx6xXlbuwsssICrstJ5ppPLGee0/Wt0fmn7QT4IQAACEIBAOxBAANoOo0wfIQABCEAAAg1KQEI86wHaWM/VroUSKHhNRmnb7bTTTuaRRx4x1nO8E5ImCfmkwdbR4ayzznJL0ct1DBRvuxziqN9fffWVs82Y1C+lbbHFFi7fggsu6JzuKN+ll16alL0ucRdeeKFzDpRVZeVy8IJJ2WGV8DgpyKmOloTHQ3wM4unRYy9UlGDu8ccfjyaF+xLWK0gIWavQp08fI2dixcLss8/ukntb+7TeyVI8/4gRI5wdV9m0lckGLaPXEv5GCDIjIfubiy++uFliiSVckyqdZzq5nHFO2/9G5pe2D+SDAAQgAAEItAsBBKDtMtL0EwIQgAAEIFAnAhJqKgRBULRG2dHcfffdnQdz78BH2nM33XRTznkSfsghioIX5MgZkdfo8gInpUsT0Qcv8Jg4caKPCrfRfGHknzteW85r8kXT/Xnxvj3//PMumzTUosE7QfJl+jR5Z1eIt12OV+QISuGEE04wo0aNcvv+n4RC0jKV0FjOjeQkRp7HFeS8RsLiaJAdSi9QLuSJPprf7/sx1HG8rz6P3z777LPOcZWc//ggT+QK4uXHwaf5bbE85XKQOQUJ79RWeVmX4x8fdJ2dfvrpzo6kF6QprdAY+POSthtssEEo2EzSeFX9ckolJ1VxTWB/7SSV66/RUqz9uRKAyn5nkm1P5VGfpUGtIGdVhYKWuu+9994uWUvlt95660JZc+ZWPJO/vsuZM9Eyvvnmm+ih2/fCzmOOOSac6+XOs1L3Cc896Rr1cT5PXgNtRDn8ks4nDgIQgAAEIACBOhKwL1oECEAAAhCAAAQgkAkBa4sxsEIBST4DK6AMrDfswGpxBXZ5rfuzArxg7NixgRUeBVabzuXbZpttwrqtACawy5kDKxgM46y3+MAKMoIhQ4aEcdqZa6653PlrrrlmYIVBgfWA7cpWmhXEBFZY6tKPOOIIRYXBCjSCWWed1aVZ79lhvN+xntZdmrU76KPCrRVquTQrbAusAC+MHzRokItXv9Uf68HdtddqtLp4a8swOPHEEwOrhejOsXYMXbwVYgbWW3xw4403BipbwTqiCaxwLizPankGVuMzWHLJJV2ctZHq8vl/EyZMCKxAzKVZTVlXt3ice+65gXWmFFjtWpdmHUwFu+22W2CFVf7UglsrmA7rt97FA+uVPrBOdQKrKejG09raDP773/8G6l+nTp1cXuXxwQpuw/OtoyYfnbMtladcDmJq7W6G9aq/VnAedO/ePejXr19gl7/n1F9sDHIyxg6sfVZXpsb6yiuvzEm1Htgdb6uJmhOva846jXJt23HHHXPSNDes1q9LO/LII3PSCh3YpephP3U9RMdU16UfPyvwdXOhUDmK15jOOOOMgXVk5MY4KW8t5ozq0T1CHK2zo+DLL78Mq7bC2EBzJn6tlzvPVGCx+8RGG23k6re2UMO6/Y7uG2qb7iO6nxQKafgVOpd4CEAAAhCAAATqR0BfygkQgAAEIAABCECgKgLWJmVgbXQGVnMvFMxIeJDmTwJAH+yS92CppZYKJHyUQFNCw/79+wcSjNql3z6b215zzTWh4Ej1WidJLt56Tw+s5/iwbgl3DjrooMDabQysvdHALlsN0ySUUh3WmVBgPY87AaEEL77d1q5nYJc6O+GMhIczzTRTmGY1DQNrc9LVaZdWB9ZjvWvPDDPMEFjv6IEEZVbrL1hllVXcOeqHXcLv8lsNS9dP1SOhnV1+HFiNM5emfx988IHrt2+HtqpbQrukIDaq37ddQk+rkemEqer/yiuv7M61mnZJp4dxdhl9MHjw4FAwFa2/2P58883nyrAaeo51lJO13xhImPTCCy+kzuMbVC4H6xwosNrE4RhZ+7HBrrvumijAKjUGvg1JW+uVPFhrrbUCCbCHDRsWnHfeecHOO+8cSFhtNWJzTrFauYE1jRC2SdecBMcSKFtt0cAu8Q7TJJg//PDDc85POnjyySed0PCoo44K7FL7QP3UvNE1oLmgdkmgLuFcmmA1ioOkjwE6t1ZzRmV7Aaj6ISGkrtO+ffsGdvl+cPLJJytLTih3nunkpPuEPsSov36+6NrWfBZXzRHdL/SRw1/zAwYMCB577LGctkQPivGL5mMfAhCAAAQgAIGOIzCJqrYPdwIEIAABCEAAAhDocAJaem01Co2WnVoNQOfoRw5xZAM0Kchxi5aRyxamXxKflK9ecVYwZqzAMbTL6evVEl8rGPWHbqtXMHl711LseJrPKHufVnDo8sjRj/dC7tPjWy1BVpkyKSCOClr67pd7x/M3y3G5HDQOn376qbNnWcyjeZoxKMbIai26peZW0GisANJYTcZi2TNL0zzR/PCOljRfNO6KsxqPOeOfplK13X4IMPbDQZrsmeWxwn9ntuD+++93TpustrCzB2wF6kXrKGeeqaBa3yc6il9RSCRCAAIQgAAEIJBDAAFoDg4OIAABCEAAAhCAAAQg0D4E5E1eNmVlL7beHxGiAlBv57fZyHckv2ZjRXshAAEIQAACHUkAJ0gdSZ+6IQABCEAAAhCAAAQg0IEE5KxJTqTqLfzswC5nWjX8MsVJYRCAAAQgAIGaEZi8ZiVTMAQgAAEIQAACEIAABCDQMATkHd461DLW5q2xdkmd+QhrD9NYx111b6PMNXhLXNbRWd3rr6TCRuJXSfs5BwIQgAAEINDOBBCAtvPo03cIQAACEIAABCAAgbYhILuo1sO6sQ63jHXu5ext3nnnnW5bbwiq34exY8eaIUOG+MOG3TYSv4aFRMMgAAEIQAACDUpgsiNtaNC20SwIQAACEIAABCAAAQhAICMC1gO9sR7WjZw29e/f34waNcoMGjQoo9LTF3P66aebCy+80DkMk4Mu2R+VLc2pp57a9OnTJ31Bdc7ZKPzq3G2qgwAEIAABCLQEAZwgtcQw0gkIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgiQBOkJKoEAcBCEAAAhCAAAQgAAEIQAACEIAABCAAAQi0BAEEoC0xjHQCAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQSCKAADSJCnEQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBASxBAANoSw0gnIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgSQCCECTqBAHAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEItAQBBKAtMYx0AgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEEgigAA0iQpxEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQEsQQADaEsNIJyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEkAghAk6gQBwEIQAACEIAABCAAAQhAAAIQgAAEIAABCLQEAQSgLTGMdAICEIAABCAAAQhAAAIQgAAEIAABCEAAAhBIIoAANIkKcRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBLEEAA2hLDSCcgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBJAIIQJOoEAcBCEAAAhCAAAQgAAEIQAACEIAABCAAAQi0BAEEoC0xjHQCAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQSCKAADSJCnEQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBASxBAANoSw0gnIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgSQCCECTqBAHAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEItAQBBKAtMYx0AgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEEgigAA0iQpxEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQEsQQADaEsNIJyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEkAghAk6gQBwEIQAACEIAABCAAAQhAAAIQgAAEIAABCLQEAQSgLTGMdAICEIAABCAAAQhAAAIQgAAEIAABCEAAAhBIIoAANIkKcRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBLEEAA2hLDSCcgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBJAIIQJOoEAcBCEAAAhCAAAQgAAEIQAACEIAABCAAAQi0BAEEoC0xjHQCAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQSCKAADSJCnEQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBASxBAANoSw0gnIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgSQCCECTqBAHAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEItAQBBKAtMYx0AgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEEgigAA0iQpxEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQEsQQADaEsNIJyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEkAghAk6gQBwEIQAACEIAABCAAAQhAAAIQgAAEIAABCLQEAQSgLTGMdAICEIAABCAAAQhAAAIQgAAEIAABCEAAAhBIIoAANIkKcRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBLEJjsSBtaoid0AgIQaCoCn3/+ufnqq6/Md999Z3744Qcz/fTTJ7Z//Pjx5ttvv3X5lFd/nTt3NpNMMkli/o6MjPbJt7XQNggCM/XUU1fd3EsvvdT8+uuvZo455qi6rEYo4I8//jCPPfaYeeedd0zv3r0za5J4jx071lx55ZXm5ZdfNrPOOquZYYYZXPnF0jJrAAVlTuDjjz821157rfntt99qev3Xq560gHSfufrqq03fvn3NpJOW/o7N9Z2WLPlajUC5c8X3X/eURx55xNx4441m4MCBPjqz7VtvvWVmnnlmV96bb75pHnroIbPgggtmVn6tCqrV87lW7a1nuRpT3ZcXX3xxM/nkk9ez6lR1lfOuWOm8SdWQEplqxfHFF1803bp1K1E7yRCAQFsQsC/GBAhAAAJ1J3DfffcF6623XmBvtO7v4osvTmzDv//972DVVVd1eewPhGDUqFGBFfgl5u3oyPvvvz/Ycsstwz716dMnWHvttcO/lVdeOZhnnnlc+s4771x1c+0PJ1eWFYRUXVYjFHDXXXcFSyyxhOvTYYcdlmmTDj/88GD33XcP7Etw0KNHj6Bnz56BFby7OoqlZdoICsuEwI8//hicdNJJwXTTTeeulcsuuyyTcuOF1KueeL2FjidMmBAccMABgf1x7fr9+++/F8qaE8/1nYODgzYgUOlcEZpHH300GDx4sJtjc801V+a0brvttuC6664LXnnllWDIkCGunhVWWCHzerIusJbP56zb2hHlbbLJJm4sb7nllo6ovmidad8Vq5k3RRtQRmKtOP7rX/8K9tprr8B+3CijNWSFAARakYBpxU7RJwhAoDkIWG2CwGpXuJfGqaaaKnjiiScSG26/Rrs8Y8aMSUxPEylhwSeffJIma1V5rLZqYLWyXHtvvfXWxLIkvNl2220T0wpFJrVf/EaOHBnccccdhU6reXxSu6qp9D//+Y9jd9RRR1VTTM65uq4kNPrpp59c/NNPPx3sv//+gfgVS8sppMqDrDlV2ZyWOP3EE09010qtBKAeUrX1ZD32Sy21VGA14H3zim7rdX0XbUSFiVlzq7AZbXNaK/IuZ67EB7pTp05B1gLQU045JfjnP/8ZVuXfbZpBAKpG1+L5HMJooh27Mimvtc8++2xw8MEHB3bVT15aR0eU+65Yzbwpp69J95xiHJO4l1OfXTFS9rt3OeWTFwIQaA4CpddOtYUeLJ2EAAQ6goCWsXft2tXMMsssbhn3BhtsYD799NO8pmi58owzzmis9mReWtqIY445xi2tTpu/0nxayj/77LO70wstg7Lan8YKfMuqIqn94nfIIYeYNdZYo6yyssyc1K5qypd5A4XJJpusmmJyzrWaua48+4PWxS+99NLGCrWcGYViaTmFVHmQNacqm9MSp3fp0sX1o9bmMKqtJ+ux1xxJOz/qdX3X4oLKmlst2thKZbYi73LmSnwsNe+zvLeceuqp5plnnjGbbbZZWJU3w5JlPWHhNdipxfO5Bs2saZGfffaZ2WqrrfLqWHLJJc2xxx7rTDTlJXZwRLnvitXMm3K6mnTPKcSxEPdy6hs+fLjR8n7NRQIEINC+BBCAtu/Y03MINAyBc845x9hlyUb2PjfaaCMzceLEvLbZ5a5mmmmmyYtPE2G1FszRRx+dJmsmeUr9mFFfzj333NR11bv9aRtWy3aVYpi2jcr36quvFsxeLK3gSWUm1JJTmU0he50J1Grs086PelzftUBaK261aGsrlNnKvNPOlVqO45dffuneQXbbbbdaVlO3shuBad06G6no559/du+oSR/qI9laYrfWY1zOPSdL7kcccYSxK6eMXe7fEuNEJyAAgfIJNJ6V5vL7wBkQgECTE+jevbu5+eabzfLLL2/sMnfzj3/8w5x33nmpevXSSy+Zq666ynz44YdOK6p///5m++23DzUsr7/+erPpppsauwTInH/++cbasTIbb7yxsfY4XfnffPONi3/qqafcOX//+9/NiBEjcuq+/fbbzYUXXui+GlejhapCrd0v89xzz5nNN988rMMuAzJ2Gbt54YUXzPfff290bG1hunYXa////vc/15+77747R6D69ddfG2tjzDmWkkD5oosuck4WVKbYyPnCRx99ZG644Qbz8MMPu7qsbUEz5ZRThm364osvjF2q745ff/11p6UrIbK1neniirVLGdJwVT5xv+KKK5wGsBy69OvXT9GpNW/++9//GmtqwDlNWnjhhc0+++wTauCKgbU16/7k1GKHHXZwZa+44oruWimUZm1QuXxp+6AxtcsanbMuu/jDrL766mattdZyjhCKcSo27q4BsX/6MGCX7psPPvjApeg63XDDDc0ZZ5zhHHYoUs6wdtllFzP//PO7OXH88ccbXQ8nnHCCscs53Xml+iVWuqasLTN37VxwwQVu/6CDDnJzVIUU4+4qKfKvGC9/mn4cqX5p7cohlrUXHI6fz1NsW6yOm266yah8zZ99993XXf/PP/+8sUvp3Y8izY+zzjqrWPEurR5zRGOu8X3ttdfcfUH3N93LSv04LXbt6/pOM8al7q2CkMW9Jg660JzR/UEfjtR/3Yc0F3QtS5tHW6Vr7lnzIq5IzUldQ4MGDTJ77rlnWE2afoWZYzuae3KMI+d2Gv+ZZprJ7LrrrjnONUrNDV1rct610korufmqdsrhzkILLWSs6Q/3MTBabam5UGos63GdRtvr9x9//HHndE4fOOV4Tg7opMWl8VHfdf+79957jeajrvNtttnG/O1vf/Onu636dskllxgx07l6/ur5bZfo5uTTQTlzpdQY5RX+Z0S592xfjp6dWsmy3HLL+ai8rTXR4q5Xa/fcPbv1vIrzKDWWKjRNG0s9A3zjqn0+p5lrdsmzm1N6P1lllVXcu5Y013UsP71+5YZvU3xbr/mk99LjjjvOzVWtSNL4TDvttOb00093TXr33Xfdu6iuzaFDh4bNLHXPSDOm1TIq9K6oRmY5b9K2s9A9Xu/lcY56J15zzTXzuFvb1ubkk092TlTVD73f6N4irWox1XuvBNUaCz0XfND49OrVy40lmqCeClsItBmB5lipTyshAIFWJbDuuusGDz74oOve5Zdf7mz62dtwEHeKJKc1cdtKVjDjbHR526FWgBjY5eeBFYQFVmjiypTNIDm/UZlyoCRj8Pbl36W98cYbgX3hDuxLfiDbnbLPpXxWSJiD2y7Nd/Fnn312TnyhA7VV5VjBaU4W2WGyGiCB/RGfE2+/SAfDhg0L46ywNbCCS3dcqP1WwOPKUj3qsw9iaZcQufqtgCywgtbALjNyzlNkm9R6KA1km9QK6ILTTjvN1asyxMgH+8IZzDfffIF9wXdRcjql9tkXzMD+uHJxhdqlxLRcrcfUYIEFFgjee+89V6YVYjvnRGqP/aHh4or9O/TQQwNrAiCQ4X7xsAKEwC5bDKzWmztN14t9AXY81Hft60/xxdJ0cto+WOG7c9JlX7hdnXLapfZvscUW7rgYp2Lj7k5O+Pf22287G7P2BT5M1XUlG3KqV87FosEKPt114uNK9cv++AjWWWcdV5b1mOquHSuscMf+GinF3deVtC3FS+fo+rdLvAP7w9EVYX/guvqtICSnSM1n9Vn3jWhIU4f9KOLOlUMSH6yAxdnW1PyNhqR66jFHrHAxsMK7wApPXHOsMMjtW+FnYD9WRJuYt1/s+k4zxmnurVnca/IabiOKzRnNq/iY65pXnFhFg+aFFVS48nx8mn75vPGt7DVaIVZ4f/nqq6/cs0b3Hh9KzQ37sSfo3bu3a6/92BasttpqgZxU+flrPwbm2KouNRdKjWU9rlPfd7+V8zDZWNY1qnHRc9UKJgIryA+s+REXJw4aSytUcg7NdK+xy27DZ4HK0r16mWWWcXk0F+S0Tk7AdG+Q7etoKGeulBojX67GInqfVXwl92yrvRZMMcUUwXbbbeeLDrdWMOV46Jk9YMAA5xhS14Tyy261FRSFedOMpTKXamOpZ4CvsNrnc5q5Nnr06MB+rHMMdI1oTuh+p7msa2ennXbyzUnc1nM+2Q+L7v1R7dI7pt4l/Xum2qFrRWmasz6UumekGdNqGRV6V1Qbs5w35bSz0D0+ieMvv/xSkLve9TRXdE/w7/Wevf3A7+6v/ji61but/A6IPwECEGg/Aqb9ukyPIQCBRiIQFYCqXfpBpJfIuFOkuABU3rz1AyEuFHnooYfc+fpBqR/AClaLwMVZTRp37P/pJdtqnvpDt/Ve2q1GThg/bty4wGqkpn5Z8gJQvRAvuuii7k8vzNbWqWtHXACqHwBWEy2sT+3eY489wuNC7VcGq62WIwBVnJxFiaHVFssRGlu7VS5eP059UF36Qa4foD6o7zo/KoQ888wzXZzVBvXZquIqYaecTMSFynIioLqt5mJYT9KO1Rxywtxomrx86txVV101Gu1+REsAmhT0AzspLc21IWGkPJH7H0Eq32pTBHPOOacT5Pr6Co1fqXH358e3El5LCOYFhEqXUFt9jwsGJMx86623wiLS9EvXhNVscVz8D3D9iNKPhXK4h5X+uZOWlz5KWHMXOd5aJciyml85RSYJJtPWYW21OV5RAagKl4ArjQC0HnNEAme1x9/H1D79yNO80f0xTSh0fRcb43LurdXeawr1odCcsTYU3bhJWBINVlsssPaXQ0dnSpMzDWtvMcxWTr/CkyI711xzjRPqSfDpg54/EtArpJ0b/kObPlD5oPHYeuutXd+sRqmPdh/oSs2FYmNZj+s0bGxsR/dh3ZPk8dwHfexSnJ434+xz1Qf/8dOu0vBR7qPb3HPPHUhI6IP6arXEXBnRjz1p50raMVJ9SQLQSu7Zuu7UZwle48ELQDWfJVDzQdeaztHHTQmUFdKOZak2pnkGVPt8LmeuSVClvup60cdWBY2z1a4OZpttNndc7F8955PaobbKSVA86F1GaVEBaKl7RtoxrZaR2pr0rpj1vCmnnYXu8Ukc1f5C3HfccUeXJtbRIGWDQo5I7QoPd46UHwgQgED7EWAJvL2jEiAAgcYhYF9mjZZNaQmUnCLJaYCWyMeDjM1reZyWzUeDjrWU0ApCndOj6JKz6LJRq73kltT07ds3xzmSFYa54rR8zy8/s4JMI8dF5QYrNAwdFNkXerccR8uT48EKSd3STqt16Zaoq51abhwP0fb7NNlF1ZK3aJBTKQWr7ZljkH+xxRZz8b5fOlCZVgvTLaW3j0B3vOyyyxotw/NmApTPashq4+xp2h9Ybt//i7YrLVctJZNdp2gdKk9L4JQWLdPXE93KeL4cwWgZrA+ys6ZgNYL1ca9kGf68+DZtH+wLvLvW7I/0sAg5vtI1q/rjId6ntOMeL8dqxRirbeGW3VuBsUv2Y2IFCc4xliKtloVb5u3NNqTtl9pphYDu/PXXX9+VrzmlUA33tLxkqsIKR0JHP7LVJa5p7FmmrcN1pop/tZ4juudYAa+xH4hyrmM5ZpETr6effrqK1v//vC80xuXcW6u915TqRHzOaPmi+m9/aBv7QzlcHqvrX0thtTzdO5nREkjNFR/K6Zc/J7rVfNVSUo2JzK7I3ISWY/v7b9q5IUd5CvYjXVi8+ilzFZq/Wp7tQ5q5UGy+1vo6jT5LfJv91l8b0Tx2FYFb3i2b37rGfVhwwQXdrtX6dFuZXdEzSOZrrIaXz+bmgsZU7wfipeeHzLmknStpxyisMLZTyT1b5isUrDAvVtpfhzIJMO+884YRuq50b9d9UGaBBg8ebNKOZbE2pn0GVPt8LmeuySa6gt5PvBkeXdN6N9M46z2h2DL4es4nP0Dx+5Lik2zUl7pnpB3Tahn59vl7lY5rMW8qaWecZRJHtVchnldxMmOje72Ws2veKOi3ga51b57ARUb+eedjmpve7FIkmV0IQKDFCSAAbfEBpnsQaDYCEnTIfp1eSqy2iLNx+MADD+R1Q/YyFay2XV6ahGgSlsjuWCEBqOxwSthpv4Dn/MDyAkrv+Tmv8DIi1Jfojzf9+NOPGt92X5TsVt5zzz3OppTsncnOnX7ox0PSy188T7HjQi+W+nEhgZ1+3FtNFCd4ku1Qq3liJMTVjzC7/NAVbTU08qqItistVwkpZWvRC9Z8oRJqpgkSdtgllM7eZTS/7EJVG9L2QfbRZHc0HmTrLSlEOSk97bjHy1pjjTWcvatLL73UCcpVrmzmDhw40AnzZddVHwL0o8AuuwxPT9svnaDrVn/+g4AvpBruaXlJEKA/5de9wPNMuvZ8u/w2bR0+f6VbXae1nCNWM8jZZrPaWnlNTDtH8k6MRRQaY39/KufeGis6URigPPF7Tfy8+HF8zihdAjBd1xKCypayhGZWy9l97NGckADUaiu7+6wEiD5U2y/NdWsKxAnmJHDV/D3ssMPCvlYzN9RGqxXphGTvv/++b7KbB2nmQqGxrPV1Gja0jJ2k55AXcPk5XmqsVJ2e7wq6r8mOYZq5Uu0YVXLPtlrprp2yG1koxO+zyqd3Fz17db4EoGnHslgb0z4Dqn0+lxq/pPezOBsvDJUwq5JQi/nk25F0X/Jp0W2pe0baMY2WGd2vhlE9502xdqZlqX4n5dUHXn2o1fNAig/6sCQb5kOGDHEfTqO8/L4XmltzED6KLQQg0EYE/l/VqY06TFchAIHGJyDNETmv0A+lRx991DlFirfa2rhzUTLmHw/SbFLwWos+PfrypB/N0srUy7U0UeJ/xX6s+PIq2UpIFRVKqQy7TNWMHTvWaXg8+eST7oePBKHxEG1/PC3NcdKPrELnybGQtUvmtDLkhEXtVkhqQzQuLVf9qNP4SFCRFKJlxtMlmNX4W/taeePmx7HY+fHy4sdp+yBBhbQs04Z4m9KOe7x8jaOcWck5kF0G6hz3SGNZwkL9oJIQSNe1nHfJqZcPafvl88e31XJPy0v16AeNtDekSXTggQeaqaeeOvHai7cxbR3x8yo5ruUc8UKTQtdX/FqqpP2Fzqnk3hovq5x7Tfzc6HFSP+X8Sx+orI1Bl1X3p/32289pC0rrR0Ijaw7DHUfLyqJfmmP6KKRnh7QJpYEo5zzVzg3fTvVLH4Z8qGYu+DJqeZ36OsrZprk2io2VHE/p3cDnSTtXshijSu7Z0nZVkJOjcoIcnilEP8amGctibUz7DKjm+aw2+7Ep5/1M52UdajGf1Mak+1Khthe6Z/j8acbU581yW895U6zd5bAslNevBPJOjXT/32qrrQpWqw8mCtbucME8JEAAAq1LAAFo644tPYNAUxCQ5mHScmEt35YgR0FaPHFBgNccTPqC6/P6PB5E9OVJgjKF6HJDF/HnP2mK1CKoDfoarmVd8jqtoKVIao+WuklrTz8Q5WH0448/zmlCtP05CRkfSINQ2m1ahq+leaVCtF1pucqsgYR00nRLCtEy4+nyvCqtQJ0rjvGgH5rWnlo8OvVx2j707t3bCa7l5TUe9AMw/uMv3qe04x4vW8fydioNY2tvzC0Ftvau3JLctdde22lCaImuPJ8qjw9p++Xzx7fVck/LS8JPfRCwTjic4DPejmLHaesoVkaatFrPEW/2Q0tAk0L8WkrKU2mcv2+Wc2+ttK5S5yX1U8IvaX7q49hjjz3mPIlvuOGGTjNUzxLdV+VZ3S+F93VU2y//XLH2md0KA6000D37SGsKo9q54duoOhZZZBF/6D4EVDoXVEitr9OwoRnvFBsr67jF3ff9svm0cyWLMarknu3Hs9DHvkLotAJGQcuoFdKOZbE2pn0GVPN8VluLjZ+fRz6P8tcqZD2ffDuT7ks+Lbr1fU26Zyhf2jGNlpnVfj3nTbE2p2WpMgrl1YoxvavKNJDMw+jd2c+7pLo/+eQTF+3NAyXlIQ4CEGhdAghAW3ds6RkEmoKA18pIauzw4cON9frqkqRxEw3eNqGEJPEgbbiuXbuG9kG1PFAhqoEhzR0JGmXryr8M+XJ0vjQxo0E/utKGJIFu/FzZL/MCMr9sW+2Rdqh+vKu/XviR1P54eWmO4wwLnSPhmb6Q+xdk5bOOP1z2aBlJ7UrL1du/u+uuu3Ka4ZdA+i/0OYmRg/9r70ygtqvG/3/6mTITssQyNFCEsoSiZIpGJRkylAyp0CAkQ2SqEJJkCZUpohCVISmzBrMiq4wZWsoQMq39/36uf/usc5/nnPvs+7nv537f532/11rPc5+zzx6u/T17OPva174uOUyI9waOTYI/NAabvDd5bsbluutZaR04conZABY3/GZCsIt2GAtuqAsnwofeO3H6iPphixBNaTnyqXbfffeIKocAoY0GT2iJNqm0Xs007etJcG+nLcGLhQubEvTfvNgBW8aJkn5VUgZ8YeoBuuqqq+KXf9hH49017aTVD1sXS91H0LhGeC1HPiM8wgZ9BD5L8Ohq362qLLidZGxdkPj6gMWU28yrr8/kOLRzCF65RvOZTTN50g7NYYQrTc25HJffkjmDeG0688wzK+YGCEE7Yxd2CjFfAk3TN0iP+QY2TjDLAk3bF8hjqdspZUxKJW2D0weYjOFIa3sjMNt4ZpMOmqSvTPuOFjNm0xaZ2y+//PKJoJLDphDqZNvdpe9yHI+lc8C08/MsxpCS8W0coEvRnyiPsan5LTmOh6Exo/Sd9pUxDUbz7DddfA6N8e06D+GOFijl4DuAtcM4yt/82XZ6jls6/+f4/jUCRmCZIqDBwmQEjIARWCEIaCGUtMhZ4Lm6yYyEEUmabHiUGfFoTriOeoW3aGnI1EnkCCc89Z588sl1mBa8kV4Lpsgje7aWFlGES5iUdHQm4cESj5KUR/6Z8NCuBUzSx2oO6v2V4C68NMNv28M5ifThlQ477LDw4i1nT5EPXsNlE6vOE8/KEn6k7HG9j38SaJGfdIynTsvFueeeG/WSvbqRcAl7I7zNl+xFRji8QXgSh385r0iyE5UoX8b6I+zAAw8M78rE6+OrBFe8zOJBHVyl4RsexvGmLltnUQ7vVosHiukk8JImbcTVx254GtaR6aSP+qSj33Ua3iMYUR8JnOtwLsY9K6mDjr6Gp1ry1nHFdOSRRybaCu9T2ml1WX04Db33OoOeCwleol7N90mdJJxJ22+/fWeqknqRkD4hoVKSgHAkn1LcRxJdf1OCl2zN1u8LD9taxCYJc8MbOl6+8UafvSXL/mLU/6ijjqqLKymDyGeddVakleOxaGc6Op2kdR3vjvcpTZJ03XXXRb5d5cyjj+y///7BI21LzhrCQ7I2R5IWghF+9NFHJ2mJ1XVvX4xr38Tte8ekKx1bpx1r2jzn+74+k5/zKxuJ4SU7vyfCpP0Z2EgLiNsRmqReIwmvv8G7szYaRh5JoJboU1Bp3yAf2phs1IW3a9JKkz1Jgyltt9123AZN0hf63uU82mnmt/0rIVrUszm38Q5k7zPJzM1IdG32RVzm30z0QW2CJDlCykHxe9BBByVpf9Yewwks7Sul70gbLgnP7MytEorU5S92zKadgEeb+F6gjhJy1uMNcaQZGBjR7zOVvsshHkvmgGnn50n6Wu6z2vzNVY1fxmb6ybgxjojz7E+UB1+0Yd4R85HsnRKc+P6AX7zSZxoaM0rf6bQYwU/Xt+Ks+80kfPaN8V04wn8f7jzLRD+i32rDPgd1/u6yyy5JzvRGntHn6TuUo03XkWe+MQJGYNVCgN0SkxEwAkZg7gjw8cOigA9GBBuy4ZZ0XKiTD2lfJjmCGBGAElFHypLs/CTZ2ErSBEwIPbfeeusk77Ej+ciLdAiFKEsez2sBijSp0h577BGCHp7xt9NOOyVpnIykZ8FVIgA95ZRTamEteSGo0JGzpF3mtN566yXZdosyeCaPsHUZ0naJjy4EaCeccEI64IAD0mmnnVY/7+JfmmH1oo/89tprr4RAFeEpC2nCZCsthLDUU46VkmyjRriO/aSTTjop8Dv88MPjg5H42jlPOvqfZCcsZaEoeB177LEJwTLCaj6iEbxAXXwRXoorgmsWspSNMBOBBvVncSwtqCRtK7LrJRYg4Ej6XN+m4FuaQtE+8nPqR7uDxj3jeWkdEMZl4TDlgC3voEl9OA2992YeXddsICDwlTbKyGME3WecccZIWL4ZqhcCAh2vrzFlM+D000/PyeN3CPeRyK2bErxoq7RdHXWONimbjiFcRiAL1twj9FxnnXWCT8YGOYGqSyopg8j0M4Tw9FMET+RL/giPaYeMO33lzKOPIHhBoC77p1FP2rpsosYYR/9kcd230BvXvkveccnYOouxpn5prYu+PtOMhjC4KWzgGX2BeaWPSurVl1bmSaJ/s7HEOEPbYJxqvoOSvpEFNoxHO+ywQ9pvv/2SnN7FWEX/bNJQX0BwOK6/zqOdNvnlGsEtcwRzJmOizBOkCy+8MMmkQpL91npsQeAlm72JOuT5hrFfNlbrDUhpg0c/Z/OSsVva/TFH0z6aNElfGXpHOv2ReMd53kAoK4dLUdxix2zmZgQzXcI8NnoQ3Gy66abRnuTcLzDTUfZmFYvn5SEeh+aAXOi083NJX2PjSVrUgbXM2sS4C38yv1PjTz+R9mxma8HvPPsThdO2aRvMG2yOwS9jA995hPOtd8wxxwSfQ2NGSf+cFqO+b0UYnGW/mZTP9hhPH+vDEV7buBPWJtmDjr7bDm/eM17TF/lebxLzPd8DfHsgDDUZASOw6iKwBlXTgG0yAkbACKzUCHAMDq+eHHVsE0fJsZOG4yK8bjbtHua40kiosNWIDax8tDY/43ittC3jWCMOFrpIH01V37Ou+JOEccRXH9Nh5xRnLziBatM4/ttxZ3XPsf+mMyh44C979KSccXyV4EoeWgTEkWTK4mgZWPBXShjz58i8du4720dpPl3xSutA+8AeaXZ40c6rC6eS997Op30vYUOlj/aRYDCkHUkAMRLevCmtVzNN+3oa3IfwklZfHGfL3qEpm3ph/7GUhsogH3DgPeR2PmkZ8+gjtG3GrnXXXTfGLpyHaSOiFIap4pWMrVMVMCZxV59pRtfiPUwB0Nab1NUnms+5Xky9aCeMfbQRbPthP649l+RyxvUNLfLDxvPxxx9fSdhVSSgW71aL8px85HcWfWEe7XSE6RnecGQee7R8A3AcHPvPfTRJXxn3jvryn2bMxrQB3yY4d+si5g/sd2JeoesbJqcZepelPJbOAdPOz4vpa7muJb8roj/h+Iy5KXsT7+OzdMwYeqd9+c8qfKn7TR+fQ2N8O90Q7thClxJDpZNE7aT1vTZLwwwK5jXa6wlt9EdY/h6oE/nCCBiBVQoBC0BXqdfpyhgBI2AEjIARMAJGwAisrAg0BTbYLzWtHggg5JJ2cqXTHWHfdPWo9dLX0v1p6TFeDiXQv3D8iI3gvo0phPk4x5MZrHrTcznUzTwaASMwWwT61UNmW45zMwJGwAgYASNgBIyAETACqzUC+eBViTOg1RqoVazyaJXhtE5mdyZ2iLSKQTHT6rg/zRTOZZUZHt9lQ76SrfpwhrfPPvv0Cj/R7pTN00p2Si38XFZv2cwagdkjYAHo7DF1jkbACBgBI2AEjIARMAJGYAECmGuB5PRtwTMHrNoIyA53aIDK4eKqXdE51s79aY5gr2RFySlnte+++1ayoVwh4GRzoY/oc7IXW9EHTUbACKzeCPgI/Or9/l17I2AEjIARMAJGwAgYgTkgIEcelZySREkc09x4440rOfKr5MV9DqW7CCOwaiHg/rRqvc9Ja8NYKidilby3V3JqGLbPJ83D8Y2AEVj9ELAAdPV7566xETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARWG0Q8BH41eZVu6JGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkZg9UPAAtDV7527xkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRmC1QeAGrxGtNrV1RY2AEVhpEPjjH/9YXX311dVf//rXwT+8fK655porDe9djPzsZz+rzjvvvGqjjTbqeuywVRQB2u83vvGN6oorrqjucIc7VDe+8Y17a/r73/++uuaaa+r2zjVpN9hggwVpfvWrX1XnnntuteGGGy549s9//rPCg/QNb3jDBc+++tWvVtdee2219tprL3i2sgbQv3FkcfLJJ1c/+tGPAsfb3OY21fvf//7qX//6V3WXu9xlkHXGk4985CPVJptsUv3f/y3fvd1J6jwIykoe4corr6xOOeWU6r///W/RO56kOpPML/Slm970ppNkHzzT1z75yU9WW2yxRZ12KetUF7JEF8uB99J5ljbV9X6mhW5e/fPPf/5z9etf/7q63e1uNy3LK216+h3zH87A7nGPe9R8XnbZZTGW3//+9++c4+qIhReTjAXL4VuzsNqOZgSMgBEwAn0IaLA3GQEjYATmjsCXv/zlJI+NSWNT/K233nppxx13rP8e+chHpvXXXz+e7bPPPnPnr7TAH//4x+mxj31s8Pnwhz+8NJnjrQIIvPvd707yPppOPfXUJO+i6Z73vGd61ate1Vmz3/zmN+lGN7pR3d5zuyddF73yla9MhxxyyMgjCQfTlltumbbddtu0+eabJxn9T1oop+uuuy5JWJpe9KIXJS2Yk7yhjqRb2W9e/epXpxe+8IXpBz/4Qbrzne+c5KU1SSAaWEmgOZb9P/3pT+llL3tZkjA44v/vf/8bG39lfijhTlGdV+Y6lPD297//PR111FHpFre4RdT3Ax/4QEmyieK05xfmkr755VnPetZEeX/9619P22yzTfB+t7vdLdLOo04TMTlB5OXA+yTzbNf7mQCO3qjz7J9HHnlk2mGHHXp5We4Pzj777LTppptGH2rPmbvvvnuEf+pTn5pJNdtjwXL91pwJGM7ECBgBI2AE0kL1kT5JqcONgBEwAjNE4BGPeEQl4VH1wQ9+MLTZ8Oa50047LSjhzW9+c/XTn/50QfjKEnDve9876rCcNO5WFuxWRj7QglpnnXUGWUNb8QUveEFofkpgF/EltK/ud7/7hXbYG9/4xpE8jjnmmGrnnXceyfsmN7lJ9bSnPW0kXr457bTTqhNPPDHfVl/72teq7bbbrnrmM59ZHXvssRF+0EEHVUcccURoPN7pTneq0DB9+ctfvqy0hr797W9XYIUmLVp4WvRWEihX973vfavXv/711QMe8IAag66LtdZaKzD40pe+VF188cXLWvtTQrqiOnfh0AwrbcPNNPO8vtnNbla95CUvqfQNXkl4vSRFt+eXt7zlLdXjH//4BWUx79BuJiE0Pj//+c9X1CPTPOqUy5r1bwnvs2xTaP6hlTeJ5/tJ5tmu9zMLzGbVP4d4AZ/jjjuu4hQAGq/aWBtKsuyea9O4+s9//lNpU2KBlueLX/zi6u53v3vFfDoLao8Fy/VbcxZYOA8jYASMgBGoKgtA3QqMgBFYYQjc6la3CoGQtOMWfARnpqT9uWSL5FzGtL8c14XWWGONabNy+hWIwB/+8Idqzz33rL7whS+M5eLf//53CBpZaGfhJwmkWRILOhZYCEezIPVvf/tb5HnBBRf0tvNmgQj8SfPABz4wgqXhGXwhZCfvTCyMWSTySxoEo2woLCeSdk51gxvcoD6CTJ1zvV/xilcUV+WWt7xl5FOcYCWMyPgxSZ27qlDahrvSzjvs1re+dRS5VONmc36R9nVn9Z773OdW+++/f+ezoUD4b/O+1HUa4mma5328z7pNve51r4sNjic84QkTsTvpPNv1fiYqsBV5Fv2zlWXn7RlnnBHmgdggYOMsb3h1Rl7GgYzZEON/k9j0Gtr4asYvuW6OBV2mY8hjOXxrltTVcYyAETACRmA8AsvXUNb4evmpETACywSB9gKyzbaOSVbvete72sG+NwIzRQC7mrvttltoUQ5ljLARjaim8DOnQSCJ3cr3vve9OajSMfdY5PGrY951eN8F2p9oi+a+8aEPfSjspB144IEjNkZ/8Ytf1LbTXvrSl1YIFsbZIO0rb0WG/+QnP5lZ8RmvmWW4zDKapA0vs6otmt2hNoEQBruOpm4EZt2mEO4dfvjh3YU5NBB45zvfWX34wx+OzTJOAWAPdFWmoT46q7oPleNvzVkh7XyMgBEwAis3AtYAXbnfj7kzAqs1ArL7FccTn/GMZ9Q4cCzspJNOiqPGHIt705veFEKf448/Po4eczSRY7QIoN7znvfENceCt9pqq8jjhz/8YSwucDCA5sGDHvSgCi0gjiNnGldGjtP3+49//CPK/PjHP16hdfC85z2vetjDHjYSnUUgPHLkFwcAu+yyS8TLkXDgMK4e8IfzDY4NX3XVVRXHgPfbb7/qjne8Y85iwe9QniRA8/HTn/508HSf+9yn4iha1mLk+Xe/+91wWsKRMhzToG2Iownew2tf+9pK9huJVvQehsqSLcfqzDPPrL7//e+HNiT3shm24Mj4UD4XXXRRYEXaRz3qUSGMROuQe3wA8g7Qttx+++2jLre97W3jXdz85jev3va2t0V92v9+/vOfR5BsbbYf1cc6ceYDgfvb3/72Ci1neIEQkrLIBbcuQgCK6YdMHBOHdt111xwUxwdl/7JCs+0rX/lKBS8IcBdDYItwlnpxjB7nE5SFRmuTSvrOX/7yl4r2TfulfXA0HedE8CobpfEOyPNjH/tYdc4558QfGNFPoK233rqSDbgKLVvZiYt82hsgHJ0E00suuaSCd/owx0a7FrizaB/B2PX/SrEaKreZZ77uq/Os2vAQT33j3pOe9KQYT+EPIcHzn//8SjZog23GOd4l2smYY8hahENjXK5z1+9ixreufIbCcMB0r3vdK8YC+hw8U8eDDz44whjvZJ802i7OaNDGmyX14c1cBiH4kp3h6jvf+U7MTxzhf+pTnzrCwjispq3T0Lg4ruwRJq+/wbQFJj/oq9SL/v2UpzylPuo8SZspmWe7eCCsBNeutF39s3S868qvK4wNIdlkjZMEbIJ94hOfiL6HyYguKp2TSTtJXOIPjRcl4xL5ZKId8+3GBiGO6jbbbLN41B63L7/88vhGw0QSZl/4DmQOoT1ChDMm0R9/+9vfVre//e3j+2cah1Fd35pRmP8ZASNgBIzAqoeA7aAaASNgBFYkAjg80ciaPve5z42woUVS0jHiJOFHHS5NnSSNnYh/6KGHJn0cJ9kRS/qAThdeeGGSDdF4JkFgkjZckqAu7nGwAkkgmnBa8a1vfSvuJVxLEvAlCfuSBJERNq4MCRsjTvufFkZRjo5tpQc/+MFJAs30mMc8Jpze4JwFJzmZcHojwWvS4jGCtCiJtO973/viXh//Y+sh22lJ3saTFkoR/+qrrw7+cRLRR0N5kg6nOzp+m3AqQ14SziUJM+pytHBJ8tQavGohHvXDeQ2On3h/vIff/e53aRZlwc9hhx2WtMjhMgjcJODLt/E7xPNnP/vZJAFH8PfsZz87wbe0j8KREDxLkBP54EQoO7igLXCd28NIgdffnH/++ZGnBM8LHkvgF89oBxBtAz5w9ECbkL29eE67OP300xek/+UvfxnvV0LB+pmEy5GuDtCFFn9JHnQT/URHxtM3v/nN5uPia9oPfMmmaaShXNnfTBIAh1OinFFJ38Eh09577x3tGwc3EjCnPffcM2nxHm2JPCVkjSwlvE8Stib6jBbDcc094bQ/+j7viP7ZpGuuuSbeH+8Rgl+uGQOk/dqMOtimS9tHzrQUq6F2mfNr/vbVuZTHoTY8xNPQuIfTIt7HE5/4xCbbcb3uuusmxrFMQ2NcjidBX+QpLbcclBYzvtWJOy765hfwkrB9pN9IOBf8fOYzn6lz0vHvaFvk0ybGPNkqHAnuqtNIhOtvhvCWlnnShk2S0ChJyJZkwzR402ZdnV2/+isyAAAV1klEQVQJVpPUqc37uDZVUnbN6PUXElaFszPaEWUxzjJmQCVtZpJ5ljy73k8JrqRtU1f/nGS8a+fXd7/vvvsm5lpIG1vxzu9617smbfosSFI6J5NwkrjEHxovSscl8oIkwEwbbrhh0qmFuNcGdDi7oy3IBnSE8Q8+6VOE0yYyMR/LBEKEX3rppRGMoyiZUUgSqOZoY3/7xoKub82xGfmhETACRsAILGsEMEJvMgJGwAisMATyRykfvQhe+EMIpV39+NhtCkBhMi/EEZywKJAmSC2o4UMWIQsClSx0ZOEizYGIg9ApCxpzhc8777woB0Ee6YfKyOmav3lhJi3SWNTlZx/96Ecjb4Q4eNqFWNQiBGsKuKR1mKQJk5MFH331IE8EPQhjMlGnpgAihzd/x2GD0E5af83oSceug/dHP/rRdXhehEsLqQ4jX7wos2CRFmqEz6IsBJfSxBopBy/nmUp5lgZNXY+8UII/hJdyHJSzi1/qgFf3IUIggXCY+LSvJuG5lnA82XYRCzmEo8RBkI0goUnSOk0Ia5uEUBkBH57eqQPvm34AScsyPfnJT45r6vWOd7wj2hiCKq6H6DnPeU7aa6+9RqIh7IS/3Ffwzl7ad8ho4403jvR4380kjc0II+8mPeQhD4n+2gzL13i0bwtA2cwAN+qaCUGENHkT/S/TUrSPEqxKy818tn+76jxtGy7ladzYynjF5pG02tO1115bs81Gjhya1PdclIxxxGsL3Ahb7PhG2i7qml8QxOSNtObGwRve8IZoo00BKHnS3mYtACXfcXhvueWWCzZI8GRPv5TWPcmLsJqkTl3vg3Iosz0uLvY9sSlCftL2JOuaStrMJPMsGXcJQEtwrZnquOjqn5OMdx1Z1kHMK3wHIXjOlPOWlnUOGvktnZNJVBq3dLwoHZcQdjI+69TDCO9sYtMWdIpnJJx4hDcFoETI31MI9dmYkOZ/fNuNJB5z0zUWjPvWHJOVHxkBI2AEjMAyRsBH4DXLmoyAEVjxCEhYU2277bbBiIQbcRRX2hALGJM2RIRxdE5CmfjDYzTEUSp95MaxqOxgIR8z1kIwjiPno/CRQP+4J44EoZU06qqHPvSh1bgycrquX46Gb7DBBvUjeNRHfngKx4v3NttsE0f/rrjiitrwP0eDqUfTFuK4elBXLQTDRiR2wjiKTjkczR1H4/LEdiTmALAjmSkf75a2LBtlgS1H+iEJi3O0CMcMAbbKODIHzaIs6snRZxz8YKKAPDFlkKmUZ47sQnhnz/YxyWuTTTapOAqPjTuOwWfi2RCBA+VzpBuv7NT97vJay1F03jfEMfIukrZceHSX0LLiiComByRYq6MS1vaMLc3bwJY+Is2xOB4poWXwjukBcOdYIccCeV+0NdqhNHSrL37xi5U0POv8mxeXXXZZJSFnJe3rZnDgLUFBJUFRhE/Sd0iQMc/OjAjD5AAkAXD8lvzDO3WzXWNGQEKaEfuo5MOxa8rCyVSmWbePUqxKy818tn/bdeZ5xnOxbbiUp3HjHuMDR9/pgxJE1G32Azoins0X5LpwvHlojMtx27+LHd/a+bTvm/OLhLnRDrVJEONKO+687vvwZhzBtAhjFHNSJo4NQxLahlmVpcIql9f8bY+L05bdzm+SNlMyzzZ5z9eluOb4Xb/j+ue04x19iTG8aY4H0zY458EcC8/aVDonk640bul4UTouScMz5qq2V3cJvSuetdsCGHcR3zmY2+C7h+8ljsBnHrri94U1x4Jx35p96R1uBIyAETACyxsBC0CX9/sz90ZglUEAIWDTU6+0jUKYhA3IJuU4a665ZjO4vuY5f3mxmB/kfPKiM4fzy4c4H9TYbUQAOlRGM23zul0mz8gPYQD2FRGAIpjiD3tYfMxLuyiyQIDVpL56SGMhFkLY3sN+HXY6dby66ls0lOSJbTAEeW2BMwK0EsL2qLQpKx3frqP38V9aFvVCeIdwBSEdwlBsf2UqzSfHb/9mYSjCkCa1F2PNZ81raSJW2AvFlic2XrGXiYBIGjvRlpq8NtNxTVsnLsJO6pEJb8vY2ZTWbQ6qfwlrh2MHE/uxCEjxHi7NtUpHE2shPAtGhKN9AlCZjQjhdrbdmgsDg4022ijfhh1Wbkr6Tp2odZEX9W28W9HG3l588cVh+xThbJsQ0DVp1u2jFKtpy23WYei6tA2X8jQ07iGof81rXhO2lblGeHDWWWfVQv/Mb+kYl+M3f6cZ35r5tK+b8wv1pJ8y3rG5s6KoD2/aOXMJY0yOA495fM52VpcKqy482uPitGW385ukzZTMs111KMW1K+2kYZOOd7RD5jiEhAgFM0nbOi7Z2MIWtE4P5Edjf7vm5L4E7bil40Vffu1xCd6xz5k3o3O69pidw8f9Iihns1onJxbtKb45FlBW37fmOD78zAgYASNgBJYvAhaALt93Z86NwCqPwBZbbFEbyp+2sjgMgnRcNhx2NPNDaxTCocGsCQctePHOi1YcHODUiQUSGh8Ico877rgFWhDj+EBwimAVTUG0NXQ0OpwlsJCZlOAHbFhQoMW4WKJ+Q8KEScrS0ejqe9/7XjjEQeOJ+rJA1PHwcBIxC5676tpemHfFyWFPf/rTK/4y4aThgAMOiEVqW9slx8m/sj8Y2itZK4dwtEEf97jHjWgA5fjtX94Xjr5YrCI41XHa0DrFmVMmtHhz285hzd8ssMaRBNqFfbSi+k6bn+x8Cn67KL+7SdpZVz5dYSVYLUW5XbwMhWUciDdLnnA2ghYazq0QJuGAjc0jBApNosxpxrhZjm9NvtrXCBSbWLWfr6h7HJEhXGazAMHgOJoXVl04TVN2O79p20x7nu3CbBJcu9IvZRhO45i/qUebZGIinMGx4YX2dSmVzMk5rxx3luNFzptxm5MWzI8yPZGD6992W6gfdFzwvSRboLGpLDul1dFHH90Ra/KgWX5rTl66UxgBI2AEjMA8Efj/52nmWaLLMgJGwAgUIsCHMdoEfDwjLJyGsvaBnCAsyCYLVHKcBRGmCJDR/0jNkUGIo/kI9vBq2qfFGhF7/mVeOX6N1ioCCI48o5m1GJKt0dBC5cglOLcJb7tyVtEOXnAPX2hVjaNJyuK4MwJZ6oagD60ftEGvvPLKapJ8xvHT9WySxVg7PZ56Zb+tOvbYYwcFK9QHUwayZVtng0ZoNt1QB/ZcyElUddBBB4VgHc0ghCV77LHHSGw0aBEc9xGaoxCao12El2co94t59502T7LpF0GYLuii/O6Won2UYLUU5XbVcygs40C8WfPEkVxItlzDHEPTfEM80L9pxrhZj2+Zp65fBLeL0ULrymuWYXkjKpsUaefNpgc0T6yabWoWZbfzm6bNwE97niWsTaW4ttPN4x7hJhuamDZp/2WNUDzCMy+WUsmcnPPKcWc9XpA/4zbzE98YXdRuC11xcpic49WnQTjK3jQRkeMs5hceZvWtuZjyncYIGAEjYATmh4AFoPPD2iUZASPQgcCQ1iBJsDGJ5uY0lAVLCB7bhIbh2muvHfZA28+mvZdDgQqbZWjYIbxjUUtZ+aMfIRhaFyU4wMuZZ54ZNuC4Rihz9tlnh604hF2LJY5Wy4N74NzMAy2kQw45JBYwzfD2Ncf50RLkyOYQlZaVj98jKETIgtYZ/GThV2k+ffx04c1xUwS+i6FTTjklhEL7779/2KMcyuOcc84J+6Y77rhjRKV9s5jLdnDHpb/kkktiMckxeigfOWzaHUU7D2FsFhp25cdxSuqMXVEEzU3CXh7vFZpF3+nCm7x5p6WElg5Cq4suuii0D5vpMCHBIjuXM+v2UYrVtOU26zR0nevajNfVhmfJEzhg3oFxlPaVhUqZh2nHuNLxjXfdtA+by2//5jhdWLXj5mPL9J1MpC8tK6eZ9hftccY9BF+My01irmLDAyrBahZ16mpTJWU3+c7X5AU1x9lp2wz5NedZ7ruoFNeutJOGlbS3nCemdzB90jf2s0nGiQjaISZXSmiSObkdd9rxol33bDOcb5UmZbM/cmbZDO69RgDM+A9OJ554YmxeICxmHCqhNl9dabq+NUvz78rPYUbACBgBI7ASIqAJwWQEjIARWCEI4MUd76AaGhd4CIUhLf6SNN3CA7YWCMGjbEBF/KZH8CbzeH6VVk/SIrYZnLSQTXiclq3MJA3M+pmOCYdX9ZNPPrkOGyqjjnj9BfWQQDNJyDniwfX8888Pb/YSWEVMvCfrqFnwj4dtLTwS9cDLsI5Ch0dxaVtG3L564Bm17WFcC5akI6dtthbc9+UpTdLAgPeAR3EdbUx4I5fAKclBTp0PZRMHr88SXEW4tEaTBLxpu+22q+NxMW1ZsjeZ4CuTBHThifzSSy+NoFKepTkcPEuImrOKXzlXinBpDtXhhNEeeW94d5fd2PrZuAvajmyAJtllXRBNtkyTbHCG1+OMGZ6zN99884R39Ux4c5cwNN+O/dUx96Tj8nUc2h/eibPXXN4JnuTbHubrBI0L2Q4NHKSNnKTdF57j4bfJyyR9h6xlUy3ylLZSXZIEZhG255571mHkm/sDntzbxDO8dTdJAubIBw/A9CstohPYSbAS4ToSmXinS9E+SrAqLbdZp+Z1V52nbcOlPJWOexKYB9ann356k/W4nmSMk+3iyAdv6JlKxjdtGCXGB/qrNpBy0gW/PKNdM2ZJi3zB83aA7JlGXPKVgC9pEyBJ6zzKIg/Z163Hd3iQcDHapwRTdVZddaofti7G4c14TpmMo29961sTHrf33nvvJBMZMZeRVQlWk9Spj/eucbGk7FZ14zaPA7vttlvSplk69dRTU2mbKZ1nKajv/ZTg2sV3Duvqn6XjXc6j/asNpiTTEu3gkXsJw6M98J3AN0sm3kPpnFwat3S8KB2X8AIvZ0VJQv1Em9dR+CRneEk20YN3vsvob5n49qBOeK3PRB5yypfkNDIHJZmbiXjaeK3D+i4W861JXnK0FHML32smI2AEjIARWDUQQFvCZASMgBGYOwLSmIvFHB+6/CHAkDZRkmOfECYh0MnP5GAn+NPR4iSbaBG+1lprJQQSWcjDRztCn5yGhWJ7gc6HNwIYOX1J2ulPCK623nrrEcHVuDLGgcQHMgJQebtOfJDLqVCSl+HUFAKRXnYsE7wjiN11112THCSlI488MoS2CMWk4Ti2Hizk119//VgwwT/CA8q7+uqre9krwQahHzhn/OCxKRQm87yAgu8ddtghBGbSBgtMEURBsypLWi8h4ACbE044IRY7OiIeZeR/QzwjwJAn5agTQmbygU95sq7rSV0uv/zyyBLhWW6LCAPGEYt2hJDSKkqyAzqyKG2mkwf1EI6TLws4ebRPOrpet9scd+edd04sKIdImk7RZtvxaOvwIsdHSV6Dk440tqN03iOUpQ2xQIVHhDqkl9bLSPySviOtrnTooYfW2MILAmsdfYx+Qf70awRocowR7YYw/ngPCEcgaXimLOjkmbR8Ut4AQdh08MEH14It2iwY04+32mqraKO5L8y6fZRiNVTuCLDX3/TVeVZteIinScY9BOyMdQgVumhojGPMo82ts8468e4Z02UHObIqGd+uueaaaK+MUQi6uki2EkfmF8o6TJtpspfbFb0OQ6hCX2A+YlMHXhmX2XRg/KAfSAMzxt/cdhFMsqnWV6c688bFEN6MUzJpEfNCLmennXZK0pascynBishDdQLPcbx3jYulZdfMXn+BAEsnF+K9Mx7mDb+hNoMADCqZZ7vej7QsI30JrhGx9a+rf15wwQXF410ru7hlk5bNS96vjp4n2fPu7FMyN5Gk3R/xiEvf04mPyKNkTs5lTxJ3aLyYdFyif8ixXtRBJxaSTLNEf5Jd4fh+kWZzzM20K74BqSebiggg+SaSGZa02WabJZlhierwHplHc9/gG6gpHM115ncx35o5fd70OuKII3KQf42AETACRmCZI7AG/GsCMRkBI2AEVhsEOG6MHU68eOPNtu3EYxogsKOJnS6Op/fly5Eqhl5pG9ZFcSSwxJM7R+Y58kx87HZJGFofp68zm+IChwUSbMTx7LZ9PC1Owg7n8ccfH17jsbu27rrrFjnt6WJpXFnUk+OS1BFbqThg6aNx+fSl6QvHoRDvpemcqCsu9jpxWsHxxOZ77IqLcy2c6Eh7KDypZ/MHzbgSYIY916Fyabd41B3n3KiZb+m1hHuVBMGRbz4225V2KftOV3l9YbRRbJTS/sATjMG3i2bZPsi/FKtZl9tVt66wcW14VjxJENjp0CTzs9gxrnR8w8EX4xNj+KxJQtWwz5vzLh2bZ80H+cGLNhFiPpHAd6SIUqxyPsRfbJ3abWqSskeY1g1mBei7mE9ojoWTtJmSebZdbvN+HK7NeCv79SRz8iRxc71nNV7k/HBExfxCO6RfMcfztzLTZZddNuiMbGXm37wZASNgBIzAKAIWgI7i4TsjYASMgBHoQaC5gJLWU08sBxsBI2AEjIARMAJLjcAkc/IkcZeab+dvBIyAETACRmBFIWAnSCsKeZdrBIyAEVhmCOQDA2jAmYyAETACRsAIGIEVh8Akc/IkcVdcjVyyETACRsAIGIGlRcAC0KXF17kbASNgBFYZBDh+DcmBwSpTJ1fECBgBI2AEjMByRGCSOXmSuMsRC/NsBIyAETACRqAEAR+BL0HJcYyAETACqzkCcoRRyRFEoIDdto033riSQ6lKXopXc2RcfSNgBIyAETAC80Vgkjl5krjzrYVLMwJGwAgYASMwXwQsAJ0v3i7NCBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGIE5IuAj8HME20UZASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBIzBfBCwAnS/eLs0IGAEjYASMgBEwAkbACBgBI2AEjIARMAJGwAgYgTkiYAHoHMF2UUbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIzBcBC0Dni7dLMwJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkZgjghYADpHsF2UETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMALzRcAC0Pni7dKMgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAEZgjAhaAzhFsF2UEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMwHwRsAB0vni7NCNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AE5oiABaBzBNtFGQEjYASMgBEwAkbACBgBI2AEjIARMAJGwAgYASMwXwQsAJ0v3i7NCBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGIE5ImAB6BzBdlFGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACMwXAQtA54u3SzMCRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJGYI4IWAA6R7BdlBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAETAC80Xg/wHoj5FuvW0ZcAAAAABJRU5ErkJggg==" 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" 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" width="672" /></p>
<pre class="r"><code># ggsave(&#39;plot1.png&#39;, plot1, &#39;png&#39;, &#39;~/Dropbox/Wayde/Research/PostDiss_Trauma/CES21/Study1_Covid&#39;,
#     width = 190, height = 127, units = &#39;mm&#39;)
# ggsave(&#39;plot2.png&#39;, plot2, &#39;png&#39;, &#39;~/Dropbox/Wayde/Research/PostDiss_Trauma/CES21/Study1_Covid&#39;,
#     width = 190, height = 127, units = &#39;mm&#39;)
# ggsave(&#39;plot3.png&#39;, plot3, &#39;png&#39;, &#39;~/Dropbox/Wayde/Research/PostDiss_Trauma/CES21/Study1_Covid&#39;,
#     width = 190, height = 127, units = &#39;mm&#39;)
# ggsave(&#39;plot4.png&#39;, plot4, &#39;png&#39;, &#39;~/Dropbox/Wayde/Research/PostDiss_Trauma/CES21/Study1_Covid&#39;,
#     width = 190, height = 127, units = &#39;mm&#39;)

# ggsave(&#39;plot1_sl.png&#39;, plot1_sl, &#39;png&#39;, &#39;~/Dropbox/Wayde/Research/PostDiss_Trauma/CES21/Study1_Covid&#39;,
#     width = 190, height = 127, units = &#39;mm&#39;)
# ggsave(&#39;plot2_sl.png&#39;, plot2_sl, &#39;png&#39;, &#39;~/Dropbox/Wayde/Research/PostDiss_Trauma/CES21/Study1_Covid&#39;,
#     width = 190, height = 127, units = &#39;mm&#39;)
# ggsave(&#39;plot3_sl.png&#39;, plot3_sl, &#39;png&#39;, &#39;~/Dropbox/Wayde/Research/PostDiss_Trauma/CES21/Study1_Covid&#39;,
#     width = 190, height = 127, units = &#39;mm&#39;)
# ggsave(&#39;plot4_sl.png&#39;, plot4_sl, &#39;png&#39;, &#39;~/Dropbox/Wayde/Research/PostDiss_Trauma/CES21/Study1_Covid&#39;,
#     width = 190, height = 127, units = &#39;mm&#39;)</code></pre>
</div>
<div id="alternative-modeling-approaches" class="section level2">
<h2>Alternative Modeling Approaches</h2>
<pre class="r"><code>df$tr_vax &lt;- ifelse((df$tr1 == 2 | df$tr1 &gt; 4 ), 1, 0)
df$tr_out &lt;- ifelse(df$gop == 1 &amp; (df$tr1 == 3 | df$tr1 == 5), 1,
             ifelse(df$gop == 0 &amp; (df$tr1 == 4 | df$tr1 == 6), 1, 0))
df2$tr_vax &lt;- ifelse((df2$trgrp == 2 | df2$trgrp &gt; 4 ), 1, 0)
df2$tr_out &lt;- ifelse(df2$gop == 1 &amp; (df2$trgrp == 3 | df2$trgrp == 5), 1,
             ifelse(df2$gop == 0 &amp; (df2$trgrp == 4 | df2$trgrp == 6), 1, 0))

m_add_blm &lt;- lm(study1_dv_1 ~ tr_vax*tr_out, data = df) 
summary(m_add_blm)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ tr_vax * tr_out, data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.68023 -0.33418  0.04675  0.31977  0.62582 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.37418    0.01418   26.39   &lt;2e-16 ***
## tr_vax         0.28731    0.02295   12.52   &lt;2e-16 ***
## tr_out         0.06907    0.03016    2.29   0.0222 *  
## tr_vax:tr_out -0.05033    0.04339   -1.16   0.2463    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3367 on 1243 degrees of freedom
##   (197 observations deleted due to missingness)
## Multiple R-squared:  0.1463, Adjusted R-squared:  0.1443 
## F-statistic: 71.03 on 3 and 1243 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>m_add_sym &lt;- lm(study1_dv2_1 ~ tr_vax*tr_out, data = df) 
summary(m_add_sym)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ tr_vax * tr_out, data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.72965 -0.22900  0.03035  0.27035  0.50585 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.72965    0.01254  58.184  &lt; 2e-16 ***
## tr_vax        -0.18269    0.02031  -8.994  &lt; 2e-16 ***
## tr_out        -0.14065    0.02669  -5.269 1.62e-07 ***
## tr_vax:tr_out  0.08784    0.03838   2.289   0.0223 *  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2981 on 1245 degrees of freedom
##   (195 observations deleted due to missingness)
## Multiple R-squared:  0.09431,    Adjusted R-squared:  0.09213 
## F-statistic: 43.22 on 3 and 1245 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>m_add_blm2 &lt;- lm(study1_dv_1 ~ tr_vax*tr_out, weights = teamweight, data = df2) 
summary(m_add_blm2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ tr_vax * tr_out, data = df2, weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.57532 -0.22012  0.06829  0.22768  1.17465 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.38253    0.01842  20.767  &lt; 2e-16 ***
## tr_vax         0.21288    0.02648   8.039 3.04e-15 ***
## tr_out         0.08815    0.03613   2.440   0.0149 *  
## tr_vax:tr_out  0.04329    0.05079   0.852   0.3943    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3255 on 844 degrees of freedom
##   (152 observations deleted due to missingness)
## Multiple R-squared:  0.1252, Adjusted R-squared:  0.122 
## F-statistic: 40.25 on 3 and 844 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>m_add_sym2 &lt;- lm(study1_dv2_1 ~ tr_vax*tr_out, weights = teamweight, data = df2) 
summary(m_add_sym2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ tr_vax * tr_out, data = df2, weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.61496 -0.19838  0.01943  0.20474  1.33760 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.674459   0.017234  39.135  &lt; 2e-16 ***
## tr_vax        -0.180023   0.024756  -7.272 8.08e-13 ***
## tr_out        -0.070337   0.033845  -2.078    0.038 *  
## tr_vax:tr_out  0.002849   0.047589   0.060    0.952    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.305 on 846 degrees of freedom
##   (150 observations deleted due to missingness)
## Multiple R-squared:  0.08796,    Adjusted R-squared:  0.08472 
## F-statistic:  27.2 on 3 and 846 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>m_add_blm_r &lt;- lm(study1_dv_1 ~ tr_vax*tr_out, data = subset(df, gop == 1)) 
summary(m_add_blm_r)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ tr_vax * tr_out, data = subset(df, 
##     gop == 1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.61260 -0.32887 -0.03028  0.29914  0.67113 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)   0.328873   0.023958  13.727  &lt; 2e-16 ***
## tr_vax        0.261409   0.037397   6.990 9.11e-12 ***
## tr_out        0.005157   0.048183   0.107    0.915    
## tr_vax:tr_out 0.017158   0.068314   0.251    0.802    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3422 on 486 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1329, Adjusted R-squared:  0.1276 
## F-statistic: 24.84 on 3 and 486 DF,  p-value: 5.759e-15</code></pre>
<pre class="r"><code>m_add_sym_r &lt;- lm(study1_dv2_1 ~ tr_vax*tr_out, data = subset(df, gop == 1)) 
summary(m_add_sym_r)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ tr_vax * tr_out, data = subset(df, 
##     gop == 1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.74044 -0.23044  0.01956  0.24809  0.45744 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.74044    0.02001  37.003  &lt; 2e-16 ***
## tr_vax        -0.17852    0.03135  -5.695 2.13e-08 ***
## tr_out        -0.10268    0.04032  -2.547   0.0112 *  
## tr_vax:tr_out  0.08333    0.05710   1.459   0.1451    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2865 on 487 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.08392,    Adjusted R-squared:  0.07827 
## F-statistic: 14.87 on 3 and 487 DF,  p-value: 2.811e-09</code></pre>
<pre class="r"><code>m_add_blm_r2 &lt;- lm(study1_dv_1 ~ tr_vax*tr_out, weights = teamweight, 
                   data = subset(df2, gop == 1)) 
summary(m_add_blm_r2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ tr_vax * tr_out, data = subset(df2, 
##     gop == 1), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.76838 -0.25255 -0.00352  0.24845  1.02217 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.309821   0.029145  10.630  &lt; 2e-16 ***
## tr_vax         0.193391   0.043684   4.427 1.32e-05 ***
## tr_out        -0.015979   0.054763  -0.292    0.771    
## tr_vax:tr_out  0.001971   0.079788   0.025    0.980    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3283 on 312 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.08289,    Adjusted R-squared:  0.07407 
## F-statistic:   9.4 on 3 and 312 DF,  p-value: 5.777e-06</code></pre>
<pre class="r"><code>m_add_sym_r2 &lt;- lm(study1_dv2_1 ~ tr_vax*tr_out, weights = teamweight,
                   data = subset(df2, gop == 1)) 
summary(m_add_sym_r2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ tr_vax * tr_out, data = subset(df2, 
##     gop == 1), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.58125 -0.13384  0.03584  0.22504  0.53256 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.66038    0.02598  25.419   &lt;2e-16 ***
## tr_vax        -0.05698    0.03886  -1.466   0.1436    
## tr_out         0.08582    0.04896   1.753   0.0806 .  
## tr_vax:tr_out -0.08402    0.07108  -1.182   0.2380    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2938 on 315 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.02842,    Adjusted R-squared:  0.01917 
## F-statistic: 3.072 on 3 and 315 DF,  p-value: 0.02802</code></pre>
<pre class="r"><code>m_add_blm_d &lt;- lm(study1_dv_1 ~ tr_vax*tr_out, data = subset(df, gop == 0)) 
summary(m_add_blm_d)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ tr_vax * tr_out, data = subset(df, 
##     gop == 0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.73337 -0.26154  0.04663  0.27846  0.57382 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.42618    0.01896  22.481   &lt;2e-16 ***
## tr_vax         0.29536    0.03110   9.497   &lt;2e-16 ***
## tr_out         0.09575    0.03877   2.470   0.0138 *  
## tr_vax:tr_out -0.08393    0.05654  -1.484   0.1382    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.3262 on 658 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.156,  Adjusted R-squared:  0.1522 
## F-statistic: 40.55 on 3 and 658 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>m_add_sym_d &lt;- lm(study1_dv2_1 ~ tr_vax*tr_out, data = subset(df, gop == 0)) 
summary(m_add_sym_d)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ tr_vax * tr_out, data = subset(df, 
##     gop == 0))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.75226 -0.22726  0.02774  0.24774  0.54439 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.75226    0.01737  43.317  &lt; 2e-16 ***
## tr_vax        -0.21175    0.02844  -7.446 3.03e-13 ***
## tr_out        -0.19839    0.03552  -5.586 3.41e-08 ***
## tr_vax:tr_out  0.11349    0.05177   2.192   0.0287 *  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2988 on 659 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.1369, Adjusted R-squared:  0.133 
## F-statistic: 34.85 on 3 and 659 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>m_add_blm_d2 &lt;- lm(study1_dv_1 ~ tr_vax*tr_out, weights = teamweight,
                   data = subset(df2, gop == 0)) 
summary(m_add_blm_d2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv_1 ~ tr_vax * tr_out, data = subset(df2, 
##     gop == 0), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.88459 -0.12385  0.05294  0.17089  0.68206 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.46349    0.02287  20.264  &lt; 2e-16 ***
## tr_vax         0.24882    0.03216   7.738 6.88e-14 ***
## tr_out         0.15622    0.04194   3.725 0.000221 ***
## tr_vax:tr_out  0.01835    0.05771   0.318 0.750704    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2712 on 444 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2231, Adjusted R-squared:  0.2179 
## F-statistic: 42.51 on 3 and 444 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code>m_add_sym_d2 &lt;- lm(study1_dv2_1 ~ tr_vax*tr_out, weights = teamweight,
                   data = subset(df2, gop == 0)) 
summary(m_add_sym_d2)</code></pre>
<pre><code>## 
## Call:
## lm(formula = study1_dv2_1 ~ tr_vax * tr_out, data = subset(df2, 
##     gop == 0), weights = teamweight)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.87793 -0.21432 -0.00809  0.15331  1.51600 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)    0.71748    0.02382  30.117  &lt; 2e-16 ***
## tr_vax        -0.29047    0.03345  -8.683  &lt; 2e-16 ***
## tr_out        -0.23309    0.04368  -5.336 1.52e-07 ***
## tr_vax:tr_out  0.10928    0.06022   1.815   0.0703 .  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Residual standard error: 0.2824 on 444 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2202, Adjusted R-squared:  0.215 
## F-statistic:  41.8 on 3 and 444 DF,  p-value: &lt; 2.2e-16</code></pre>
<pre class="r"><code># creating table D.4
modelsummary(models = list(&#39;Blame&#39;  = m_add_blm,
                           &#39;Sympathy&#39; = m_add_sym,
                           &#39;Blame&#39;  = m_add_blm2,
                           &#39;Sympathy&#39; = m_add_sym2),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code># creating table D.5
modelsummary(models = list(&#39;Blame&#39;  = m_add_blm_d,
                           &#39;Sympathy&#39; = m_add_sym_d,
                           &#39;Blame&#39;  = m_add_blm_d2,
                           &#39;Sympathy&#39; = m_add_sym_d2),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code># creating table D.6
modelsummary(models = list(&#39;Blame&#39;  = m_add_blm_r,
                           &#39;Sympathy&#39; = m_add_sym_r,
                           &#39;Blame&#39;  = m_add_blm_r2,
                           &#39;Sympathy&#39; = m_add_sym_r2),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
</div>
<div id="code-graveyard" class="section level2">
<h2>Code Graveyard</h2>
<pre class="r"><code>df1_1 &lt;- group.CI(study1_dv_1 ~ tr1,
                   data = df,
                   ci = 0.90)
df1_1_dem &lt;- group.CI(study1_dv_1 ~ tr1,
                   data = subset(df, gop == 0),
                   ci = 0.90)
df1_1_gop &lt;- group.CI(study1_dv_1 ~ tr1,
                   data = subset(df, gop == 1),
                   ci = 0.90)

df1_2 &lt;- group.CI(study1_dv_2 ~ tr1,
                   data = df,
                   ci = 0.90)
df1_2_dem &lt;- group.CI(study1_dv_2 ~ tr1,
                   data = subset(df, gop == 0),
                   ci = 0.90)
df1_2_gop &lt;- group.CI(study1_dv_2 ~ tr1,
                   data = subset(df, gop == 1),
                   ci = 0.90)

df1_3 &lt;- group.CI(study1_dv_3 ~ tr1,
                   data = df,
                   ci = 0.90)
df1_3_dem &lt;- group.CI(study1_dv_3 ~ tr1,
                   data = subset(df, gop == 0),
                   ci = 0.90)
df1_3_gop &lt;- group.CI(study1_dv_3 ~ tr1,
                   data = subset(df, gop == 1),
                   ci = 0.90)

df1_sym &lt;- group.CI(study1_dv2_1 ~ tr1,
                   data = df,
                   ci = 0.90)
df1_sym_dem &lt;- group.CI(study1_dv2_1 ~ tr1,
                   data = subset(df, gop == 0),
                   ci = 0.90)
df1_sym_gop &lt;- group.CI(study1_dv2_1 ~ tr1,
                   data = subset(df, gop == 1),
                   ci = 0.90)

names(df1_1) &lt;- c(&#39;group&#39;, &#39;hi&#39;, &#39;mean&#39;, &#39;lo&#39;)
names(df1_1_dem) &lt;- c(&#39;group&#39;, &#39;hi&#39;, &#39;mean&#39;, &#39;lo&#39;)
names(df1_1_gop) &lt;- c(&#39;group&#39;, &#39;hi&#39;, &#39;mean&#39;, &#39;lo&#39;)

names(df1_2) &lt;- c(&#39;group&#39;, &#39;hi&#39;, &#39;mean&#39;, &#39;lo&#39;)
names(df1_2_dem) &lt;- c(&#39;group&#39;, &#39;hi&#39;, &#39;mean&#39;, &#39;lo&#39;)
names(df1_2_gop) &lt;- c(&#39;group&#39;, &#39;hi&#39;, &#39;mean&#39;, &#39;lo&#39;)

names(df1_3) &lt;- c(&#39;group&#39;, &#39;hi&#39;, &#39;mean&#39;, &#39;lo&#39;)
names(df1_3_dem) &lt;- c(&#39;group&#39;, &#39;hi&#39;, &#39;mean&#39;, &#39;lo&#39;)
names(df1_3_gop) &lt;- c(&#39;group&#39;, &#39;hi&#39;, &#39;mean&#39;, &#39;lo&#39;)

names(df1_sym) &lt;- c(&#39;group&#39;, &#39;hi&#39;, &#39;mean&#39;, &#39;lo&#39;)
names(df1_sym_dem) &lt;- c(&#39;group&#39;, &#39;hi&#39;, &#39;mean&#39;, &#39;lo&#39;)
names(df1_sym_gop) &lt;- c(&#39;group&#39;, &#39;hi&#39;, &#39;mean&#39;, &#39;lo&#39;)

df1_1$dv &lt;- rep(&#39;Victim&#39;, 6)
df1_1_dem$dv &lt;- rep(&#39;Victim&#39;, 6)
df1_1_gop$dv &lt;- rep(&#39;Victim&#39;, 6)

df1_2$dv &lt;- rep(&#39;Govt&#39;, 6)
df1_2_dem$dv &lt;- rep(&#39;Govt&#39;, 6)
df1_2_gop$dv &lt;- rep(&#39;Govt&#39;, 6)

df1_3$dv &lt;- rep(&#39;Activists&#39;, 6)
df1_3_dem$dv &lt;- rep(&#39;Activists&#39;, 6)
df1_3_gop$dv &lt;- rep(&#39;Activists&#39;, 6)

df1_sym$dv &lt;- rep(&#39;Sympathy&#39;, 6)
df1_sym_dem$dv &lt;- rep(&#39;Sympathy&#39;, 6)
df1_sym_gop$dv &lt;- rep(&#39;Sympathy&#39;, 6)

dat1 &lt;- bind_rows(df1_1,df1_2, df1_3, df1_sym)
dat1_dem &lt;- bind_rows(df1_1_dem, df1_2_dem, df1_3_dem, df1_sym_dem)
dat1_gop &lt;- bind_rows(df1_1_gop, df1_2_gop, df1_3_gop, df1_sym_gop)</code></pre>
<pre class="r"><code>## Plotting

cbPalette &lt;- c(&quot;#CC79A7&quot;, &quot;#009E73&quot;, &quot;#F0E442&quot;, &quot;#0072B2&quot;, &quot;#D55E00&quot;)

plot1.1 &lt;- ggplot(data = dat1, aes(x = group, y = mean, fill = dv)) +
  theme_bw() + scale_fill_manual(values = cbPalette) +
  geom_bar(stat=&quot;identity&quot;, position=position_dodge()) +
  geom_errorbar(aes(ymin = lo, ymax = hi),
                size = 0.5, width = 0.1,
                position = position_dodge(0.9)) + 
  scale_x_discrete(name = &#39;\nTreatment Condition&#39;, 
                   labels = c(&#39;Control&#39;, &#39;Anti-Vax&#39;, &#39;Democrat&#39;, &#39;Republican&#39;, &#39;Anti-Vax Dem&#39;, &#39;Anti-Vax Rep&#39;)) +
  coord_cartesian(ylim=c(20, 85)) +
  ylab(&#39;Average Blame/Sympathy&#39;) +
  labs(title = &quot;Effect of Party and Anti-Vax on Blame Attribution&quot;) +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.position=&quot;bottom&quot;) +
  theme(text=element_text(family=&quot;serif&quot;)) +
  guides(fill=guide_legend(title=&quot;Blame/Sympathy&quot;))
plot1.1

plot1.dem &lt;- ggplot(data = dat1_dem, aes(x = group, y = mean, fill = dv)) +
  theme_bw() + scale_fill_manual(values = cbPalette) +
  geom_bar(stat=&quot;identity&quot;, position=position_dodge()) +
  geom_errorbar(aes(ymin = lo, ymax = hi),
                size = 0.5, width = 0.1,
                position = position_dodge(0.9)) + 
  scale_x_discrete(name = &#39;\nTreatment Condition&#39;, 
                   labels = c(&#39;Control&#39;, &#39;Anti-Vax&#39;, &#39;Democrat&#39;, &#39;Republican&#39;, &#39;Anti-Vax Dem&#39;, &#39;Anti-Vax Rep&#39;)) +
  coord_cartesian(ylim=c(20, 85)) +
  ylab(&#39;Average Blame/Sympathy&#39;) +
  labs(title = &quot;Effect of Party and Anti-Vax on Blame Attribution, Democrats only&quot;) +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.position=&quot;bottom&quot;) +
  theme(text=element_text(family=&quot;serif&quot;)) +
  guides(fill=guide_legend(title=&quot;Blame/Sympathy&quot;))
plot1.dem

plot1.gop &lt;- ggplot(data = dat1_gop, aes(x = group, y = mean, fill = dv)) +
  theme_bw() + scale_fill_manual(values = cbPalette) +
  geom_bar(stat=&quot;identity&quot;, position=position_dodge()) +
  geom_errorbar(aes(ymin = lo, ymax = hi),
                size = 0.5, width = 0.1,
                position = position_dodge(0.9)) + 
  scale_x_discrete(name = &#39;\nTreatment Condition&#39;, 
                   labels = c(&#39;Control&#39;, &#39;Anti-Vax&#39;, &#39;Democrat&#39;, &#39;Republican&#39;, &#39;Anti-Vax Dem&#39;, &#39;Anti-Vax Rep&#39;)) +
  coord_cartesian(ylim=c(20, 85)) +
  ylab(&#39;Average Blame/Sympathy&#39;) +
  labs(title = &quot;Effect of Party and Anti-Vax on Blame Attribution, Republicans only&quot;) +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.position=&quot;bottom&quot;) +
  theme(text=element_text(family=&quot;serif&quot;)) +
  guides(fill=guide_legend(title=&quot;Blame/Sympathy&quot;))
plot1.gop</code></pre>
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